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Ranked List Truncation for Re-ranking (RLT4Reranking)

Supplementary materials for the paper Ranked List Truncation for Large Language Model-based Re-Ranking, which has been published at The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024) as a long paper.

In this paper, we have reproduced numerous ranked list truncation (RLT) methods in a "retrieve-then-re-rank" setup.

We kindly ask you to cite our papers if you find this repository useful:

@inproceedings{meng2024ranked,
  title={Ranked List Truncation for Large Language Model-based Re-Ranking},
  author={Meng, Chuan and Arabzadeh, Negar and Askari, Arian and Aliannejadi, Mohammad and de Rijke, Maarten},
  booktitle={SIGIR},
  year={2024},
}

This repository is structured into four distinct parts:

  1. Prerequisites
  2. Data preparation
    • 2.1 Download raw data
    • 2.2 Obtain retrieved lists
    • 2.3 Obtain re-ranked lists
    • 2.4 Feature generation
    • 2.5 Training label generation
  3. Reproducing results
    • 3.1 Unsupervised RLT methods
    • 3.2 Supervised RLT methods
    • 3.3 Evaluation
  4. Reproducing plots
  5. Results on Robust04 (we report results on Robust04 here due to the limited space in our paper)

1. Prerequisites

We recommend executing all processes in a Linux environment.

pip install -r requirements.txt

2. Data preparation

We conduct experiments on TREC 2019 and 2020 deep learning (TREC-DL) and TREC 2004 Robust (Robust04) tracks.

2.1 Download raw data

All raw data would be stored in the ./datesets directory.

2.1.1 TREC-DL 19 and 20

Download queries and qrels for TREC-DL 19 and 20, as well as the MS MARCO V1 passage ranking collection:

# queries
mkdir datasets/msmarco-v1-passage/queries 
wget -P ./datasets/msmarco-v1-passage/queries/ https://msmarco.z22.web.core.windows.net/msmarcoranking/msmarco-test2019-queries.tsv.gz
wget -P ./datasets/msmarco-v1-passage/queries/ https://msmarco.z22.web.core.windows.net/msmarcoranking/msmarco-test2020-queries.tsv.gz
gzip -d ./datasets/msmarco-v1-passage/queries/*.tsv.gz
mv ./datasets/msmarco-v1-passage/queries/msmarco-test2019-queries.tsv ./datasets/msmarco-v1-passage/queries/dl-19-passage.queries-original.tsv
mv ./datasets/msmarco-v1-passage/queries/msmarco-test2020-queries.tsv ./datasets/msmarco-v1-passage/queries/dl-20-passage.queries-original.tsv 

# qrels
mkdir datasets/msmarco-v1-passage/qrels
wget -P ./datasets/msmarco-v1-passage/qrels/ https://trec.nist.gov/data/deep/2019qrels-pass.txt
wget -P ./datasets/msmarco-v1-passage/qrels/ https://trec.nist.gov/data/deep/2020qrels-pass.txt
mv ./datasets/msmarco-v1-passage/qrels/2019qrels-pass.txt ./datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt
mv ./datasets/msmarco-v1-passage/qrels/2020qrels-pass.txt ./datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt

# collection
mkdir datasets/
mkdir datasets/msmarco-v1-passage/ 
mkdir datasets/msmarco-v1-passage/collection
wget -P ./datasets/msmarco-v1-passage/collection/ https://msmarco.z22.web.core.windows.net/msmarcoranking/collection.tar.gz --no-check-certificate
tar -zxvf  ./datasets/msmarco-v1-passage/collection/collection.tar.gz  -C ./datasets/msmarco-v1-passage/collection/
mv ./datasets/msmarco-v1-passage/collection/collection.tsv ./datasets/msmarco-v1-passage/collection/msmarco.tsv

2.1.2 Robust04

We follow ir_datasets to fetch Robust04 queries and the collection; please first follow this instruction before executing the following commands:

# queries & collection
mkdir datasets/robust04/
mkdir datasets/robust04/collection
mkdir datasets/robust04/queries

python -u process_robust04.py \
--mode download
--query_output_path ./datasets/robust04/queries/robust04.query-title.tsv \
--collection_output_path ./datasets/robust04/collection/robust04.json

# qrels
mkdir datasets/robust04/qrels
wget -P ./datasets/robust04/qrels/ https://trec.nist.gov/data/robust/qrels.robust2004.txt
mv ./datasets/robust04/qrels/qrels.robust2004.txt ./datasets/robust04/qrels/robust04.qrels.txt

2.2 Obtain retrieved lists

We consider three retrievers: BM25, SPLADE++ ("EnsembleDistil") and RepLLaMA (7B). We use Pyserini to get the retrieved lists returned by BM25 and SPLADE++. For RepLLaMA, we use the retrieved lists shared by the original author. Note that we rely on publicly available indexes to increase our paper's reproducibility; for Robust04, we only consider BM25 because RepLLaMA and SPLADE++'s indexes are not publicly available at the time of writing; we follow this document to perform BM25.

All retrieved lists would be stored in the directory datasets/msmarco-v1-passage/runs or datasets/robust04/runs.

2.2.1 BM25

Use the following commands to get BM25 ranking results on TREC-DL 19, TREC-DL 20 and Robust04:

# TREC-DL 19
python -m pyserini.search.lucene \
  --threads 16 --batch-size 128 \
  --index msmarco-v1-passage-full \
  --topics datasets/msmarco-v1-passage/queries/dl-19-passage.queries-original.tsv \
  --output datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-bm25-1000.txt \
  --bm25 --k1 0.9 --b 0.4 --hits 1000

# TREC-DL 20
python -m pyserini.search.lucene \
  --threads 16 --batch-size 128 \
  --index msmarco-v1-passage-full \
  --topics datasets/msmarco-v1-passage/queries/dl-20-passage.queries-original.tsv \
  --output datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-bm25-1000.txt \
  --bm25 --k1 0.9 --b 0.4 --hits 1000

# Robust04
python -m pyserini.search.lucene \
  --threads 16 --batch-size 128 \
  --topics ./datasets/robust04/queries/robust04.query-title.tsv \
  --index robust04 \
  --output ./datasets/robust04/runs/robust04.run-title-bm25-1000.txt \
  --hits 1000 --bm25

2.2.2 SPLADE++

Use the following commands to get SPLADE++ ranking results on TREC-DL 19 and 20:

# TREC-DL 19
python -m pyserini.search.lucene \
  --threads 16 --batch-size 128 \
  --index msmarco-v1-passage-splade-pp-ed \
  --topics ./datasets/msmarco-v1-passage/queries/dl-19-passage.queries-original.tsv \
  --encoder naver/splade-cocondenser-ensembledistil \
  --output ./datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-splade-pp-ed-pytorch-1000.txt \
  --hits 1000 --impact

# TREC-DL 20
python -m pyserini.search.lucene \
  --threads 16 --batch-size 128 \
  --index msmarco-v1-passage-splade-pp-ed \
  --topics ./datasets/msmarco-v1-passage/queries/dl-20-passage.queries-original.tsv \
  --encoder naver/splade-cocondenser-ensembledistil \
  --output ./datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-splade-pp-ed-pytorch-1000.txt \
  --hits 1000 --impact

2.2.3 RepLLaMA

Use the following commands to get RepLLaMA ranking results on TREC-DL 19 and 20:

# TREC-DL 19
wget https://www.dropbox.com/scl/fi/byty1lk2um36imz0788yd/run.repllama.psg.dl19.txt?rlkey=615ootx2mia42cxdilp4tvqzh -O ./datasets/msmarco-v1-passage/runs/run.repllama.psg.dl19.txt

python -u format.py \
--input_path ./datasets/msmarco-v1-passage/runs/run.repllama.psg.dl19.txt \
--output_path ./datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-repllama-1000.txt \
--ranker_name repllama

# TREC-DL 20
wget https://www.dropbox.com/scl/fi/drgg9vj8mxe3qwayggj9o/run.repllama.psg.dl20.txt?rlkey=22quuq5wzvn6ip0c5ml6ad5cs -O ./datasets/msmarco-v1-passage/runs/run.repllama.psg.dl20.txt

python -u format.py \
--input_path ./datasets/msmarco-v1-passage/runs/run.repllama.psg.dl20.txt \
--output_path ./datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-repllama-1000.txt \
--ranker_name repllama

2.3 Obtain re-ranked lists

We consider RankLLaMA (7B) and MonoT5 as re-rankers. We use Tevatron to perform RankLLaMA. We already put the source code of Tevatron in the current directory and made some modifications to it for better adapting Robust04. So please install Tevatron by its source code:

cd tevatron
pip install --editable .
cd ..

We use MonoT5 from PyGaggle. Please first install it by following the PyGaggle documentation. Make sure to clone PyGaggle in the current directory:

git clone --recursive https://github.com/castorini/pygaggle.git

Note that PyGaggle requires earlier versions of packages (i.e., Pyserini), so we suggest installing PyGaggle in a separate conda environment. Note that using MonoT5 to re-rank the retrieved list returned by RepLLaMA and Splade++ yields worse results; hence we only consider the pipeline of BM25--MonoT5.

All re-ranked lists would be stored in the directory datasets/msmarco-v1-passage/runs or datasets/robust04/runs.

Note that we recommend using GPU to execute all commands in this section.

2.3.1 BM25--RankLLaMA

Note that Robust04 is a document-based corpus, so we use RankLLaMA's checkpoint ("castorini/rankllama-v1-7b-lora-doc") trained on the MS MARCO v1 document corpus, and set the max length of a document to 2048. Use the following commands to use RankLLaMA to re-rank the retrieved list returned by BM25 on TREC-DL 19 and 20 and Robust04:

# TREC-DL 19 
mkdir ./datasets/msmarco-v1-passage/runs/rankllama_input/

python -u ./tevatron/examples/rankllama/prepare_rerank_file.py \
--query_data_name Tevatron/msmarco-passage \
--query_data_split dl19 \
--corpus_data_name Tevatron/msmarco-passage-corpus \
--retrieval_results ./datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-bm25-1000.txt \
--output_path ./datasets/msmarco-v1-passage/runs/rankllama_input/rerank_input.dl-19-passage.run-original-bm25-1000.jsonl \
--depth 1000

python -u ./tevatron/examples/rankllama/reranker_inference.py \
  --output_dir=temp \
  --model_name_or_path castorini/rankllama-v1-7b-lora-passage \
  --tokenizer_name meta-llama/Llama-2-7b-hf \
  --encode_in_path ./datasets/msmarco-v1-passage/runs/rankllama_input/rerank_input.dl-19-passage.run-original-bm25-1000.jsonl \
  --fp16 \
  --per_device_eval_batch_size 64 \
  --q_max_len 32 \
  --p_max_len 164 \
  --dataset_name json \
  --encoded_save_path ./datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-bm25-1000-rankllama-1000.txt

# TREC-DL 20
python -u ./tevatron/examples/rankllama/prepare_rerank_file.py \
--query_data_name Tevatron/msmarco-passage \
--query_data_split dl20 \
--corpus_data_name Tevatron/msmarco-passage-corpus \
--retrieval_results ./datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-bm25-1000.txt \
--output_path ./datasets/msmarco-v1-passage/runs/rankllama_input/rerank_input.dl-20-passage.run-original-bm25-1000.jsonl \
--depth 1000

python -u ./tevatron/examples/rankllama/reranker_inference.py \
  --output_dir=temp \
  --model_name_or_path castorini/rankllama-v1-7b-lora-passage \
  --tokenizer_name meta-llama/Llama-2-7b-hf \
  --encode_in_path ./datasets/msmarco-v1-passage/runs/rankllama_input/rerank_input.dl-20-passage.run-original-bm25-1000.jsonl \
  --fp16 \
  --per_device_eval_batch_size 64 \
  --q_max_len 32 \
  --p_max_len 164 \
  --dataset_name json \
  --encoded_save_path ./datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-bm25-1000-rankllama-1000.txt

# Robust04
mkdir ./datasets/robust04/runs/rankllama_input/

python -u ./tevatron/examples/rankllama/prepare_rerank_file.py \
--query_path ./datasets/robust04/queries/robust04.query-title.tsv \
--corpus_path ./datasets/robust04/collection/robust04.json \
--retrieval_results ./datasets/robust04/runs/robust04.run-title-bm25-1000.txt \
--output_path ./datasets/robust04/runs/rankllama_input/rerank_input.robust04.run-title-bm25-1000.jsonl \
--depth 1000

python -u ./tevatron/examples/rankllama/reranker_inference.py \
  --output_dir=temp \
  --model_name_or_path castorini/rankllama-v1-7b-lora-doc \
  --tokenizer_name meta-llama/Llama-2-7b-hf \
  --encode_in_path ./datasets/robust04/runs/rankllama_input/rerank_input.robust04.run-title-bm25-1000.jsonl \
  --fp16 \
  --per_device_eval_batch_size 8 \
  --q_max_len 32 \
  --p_max_len 2048 \
  --dataset_name json \
  --encoded_save_path ./datasets/robust04/runs/robust04.run-title-bm25-1000-rankllama-doc-2048-1000.txt

2.3.2 SPLADE++—-RankLLaMA

Use the following commands to use RankLLaMA to re-rank the retrieved list returned by SPLADE++ on TREC-DL 19 and 20:

# TREC-DL 19
python -u ./tevatron/examples/rankllama/prepare_rerank_file.py \
--query_data_name Tevatron/msmarco-passage \
--query_data_split dl19 \
--corpus_data_name Tevatron/msmarco-passage-corpus \
--retrieval_results ./datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-splade-pp-ed-pytorch-1000.txt \
--output_path ./datasets/msmarco-v1-passage/runs/rankllama_input/rerank_input.dl-19-passage.run-original-splade-pp-ed-pytorch-1000.jsonl \
--depth 1000

python -u ./tevatron/examples/rankllama/reranker_inference.py \
  --output_dir=temp \
  --model_name_or_path castorini/rankllama-v1-7b-lora-passage \
  --tokenizer_name meta-llama/Llama-2-7b-hf \
  --encode_in_path ./datasets/msmarco-v1-passage/runs/rankllama_input/rerank_input.dl-19-passage.run-original-splade-pp-ed-pytorch-1000.jsonl \
  --fp16 \
  --per_device_eval_batch_size 64 \
  --q_max_len 32 \
  --p_max_len 164 \
  --dataset_name json \
  --encoded_save_path ./datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-splade-pp-ed-pytorch-1000-rankllama-1000.txt

# TREC-DL 20
python -u ./tevatron/examples/rankllama/prepare_rerank_file.py \
--query_data_name Tevatron/msmarco-passage \
--query_data_split dl20 \
--corpus_data_name Tevatron/msmarco-passage-corpus \
--retrieval_results ./datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-splade-pp-ed-pytorch-1000.txt \
--output_path ./datasets/msmarco-v1-passage/runs/rankllama_input/rerank_input.dl-20-passage.run-original-splade-pp-ed-pytorch-1000.jsonl \
--depth 1000

python ./tevatron/examples/rankllama/reranker_inference.py \
  --output_dir=temp \
  --model_name_or_path castorini/rankllama-v1-7b-lora-passage \
  --tokenizer_name meta-llama/Llama-2-7b-hf \
  --encode_in_path ./datasets/msmarco-v1-passage/runs/rankllama_input/rerank_input.dl-20-passage.run-original-splade-pp-ed-pytorch-1000.jsonl \
  --fp16 \
  --per_device_eval_batch_size 64 \
  --q_max_len 32 \
  --p_max_len 164 \
  --cache_dir /gpfs/work3/0/guse0654/cache/ \
  --dataset_name json \
  --encoded_save_path ./datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-splade-pp-ed-pytorch-1000-rankllama-1000.txt

2.3.3 RepLLaMA--RankLLaMA

Use the following commands to use RankLLaMA to re-rank the retrieved list returned by RepLLaMA on TREC-DL 19 and 20:

# TREC-DL 19
python -u ./tevatron/examples/rankllama/prepare_rerank_file.py \
--query_data_name Tevatron/msmarco-passage \
--query_data_split dl19 \
--corpus_data_name Tevatron/msmarco-passage-corpus \
--retrieval_results ./datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-repllama-1000.txt \
--output_path ./datasets/msmarco-v1-passage/runs/rankllama_input/rerank_input.dl-19-passage.run-original-repllama-1000.jsonl \
--depth 1000

python -u ./tevatron/examples/rankllama/reranker_inference.py \
  --output_dir=temp \
  --model_name_or_path castorini/rankllama-v1-7b-lora-passage \
  --tokenizer_name meta-llama/Llama-2-7b-hf \
  --encode_in_path ./datasets/msmarco-v1-passage/runs/rankllama_input/rerank_input.dl-19-passage.run-original-repllama-1000.jsonl \
  --fp16 \
  --per_device_eval_batch_size 64 \
  --q_max_len 32 \
  --p_max_len 164 \
  --dataset_name json \
  --encoded_save_path ./datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-repllama-1000-rankllama-1000.txt

# TREC-DL 20
python -u ./tevatron/examples/rankllama/prepare_rerank_file.py \
--query_data_name Tevatron/msmarco-passage \
--query_data_split dl20 \
--corpus_data_name Tevatron/msmarco-passage-corpus \
--retrieval_results ./datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-repllama-1000.txt \
--output_path ./datasets/msmarco-v1-passage/runs/rankllama_input/rerank_input.dl-20-passage.run-original-repllama-1000.jsonl \
--depth 1000

python -u ./tevatron/examples/rankllama/reranker_inference.py \
  --output_dir=temp \
  --model_name_or_path castorini/rankllama-v1-7b-lora-passage \
  --tokenizer_name meta-llama/Llama-2-7b-hf \
  --encode_in_path ./datasets/msmarco-v1-passage/runs/rankllama_input/rerank_input.dl-20-passage.run-original-repllama-1000.jsonl \
  --fp16 \
  --per_device_eval_batch_size 64 \
  --q_max_len 32 \
  --p_max_len 164 \
  --dataset_name json \
  --encoded_save_path ./datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-repllama-1000-rankllama-1000.txt

2.3.4 BM25--MonoT5

Note that we follow this document to run MonoT5 on Robust04; to deal with the long documents in Robust04, MonoT5 uses the MaxP technique. Use the following commands to use MonoT5 to re-rank BM25 results on TREC-DL 19 and 20, as well as Robust04:

# TREC-DL 19
python -u monot5.py \
--query_path  ./datasets/msmarco-v1-passage/queries/dl-19-passage.queries-original.tsv \
--run_path ./datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-bm25-1000.txt \
--index_path msmarco-v1-passage-full \
--model castorini/monot5-base-msmarco \
--k 1000

# TREC-DL 20
python -u monot5.py \
--query_path  ./datasets/msmarco-v1-passage/queries/dl-20-passage.queries-original.tsv \
--run_path ./datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-bm25-1000.txt \
--index_path msmarco-v1-passage-full \
--model castorini/monot5-base-msmarco \
--k 1000

# Robust04
wget -P ./datasets/robust04/collection/ https://storage.googleapis.com/castorini/robust04/trec_disks_4_and_5_concat.txt --no-check-certificate

python ./pygaggle/pygaggle/run/robust04_reranker_pipeline_gpu.py \
--queries ./datasets/robust04/queries/04.testset \
--run ./datasets/robust04/runs/robust04.run-title-bm25-1000.txt \
--corpus ./datasets/robust04/collection/trec_disks_4_and_5_concat.txt \
--output_monot5 ./datasets/robust04/runs/robust04.run-title-bm25-1000-monot5-1000.txt

2.4 Feature generation

We need first to build tf-idf and doc2vec models for collections, and then to infer features for retrieved lists. We use identical item features to eliminate confounding factors from the input; each item is represented by its retrieval score, length, unique token count, and the cosine similarity between its tf-idf/doc2vec vector and the vectors of its adjacent items.

Note that be aware that for Robust04 (250 queries), the RLT literature usually randomly divides the dataset into a training set (80% queries) and a test set (20% queries). To eliminate the impact of the random data division on results, we employ 5-fold cross-validation on Robust04, generating 5 feature sets, each containing approximately 50 queries.

Please first create the folder where feature files would be produced.

mkdir datasets/msmarco-v1-passage/features
mkdir datasets/robust04/features

2.4.1 Build tf-idf models for collections:

Use the following commands to build tf-idf models for MS MARCO V1 passage ranking and Robust04 collections:

# MS MARCO V1 passage ranking
python -u ./rlt/features.py \
--index_path ./datasets/msmarco-v1-passage/collection/msmarco.tsv \
--output_path ./datasets/msmarco-v1-passage/features/ \
--mode tfidf 

# Robust04
python -u ./rlt/features.py \
--index_path ./datasets/robust04/collection/robust04.json \
--output_path ./datasets/robust04/features/ \
--mode tfidf

2.4.2 Build doc2vec models for collections:

Use the following commands to train doc2vec models for MS MARCO V1 passage ranking and Robust04 collections:

# MS MARCO V1 passage ranking
python -u ./rlt/features.py \
--index_path ./datasets/msmarco-v1-passage/collection/msmarco.tsv \
--output_path ./datasets/msmarco-v1-passage/features/ \
--mode doc2vec --vector_size 128 

# Robust04
python -u ./rlt/features.py \
--index_path ./datasets/robust04/collection/robust04.json \
--output_path ./datasets/robust04/features/ \
--mode doc2vec --vector_size 128

2.4.3 Generate features for BM25 ranking results

Note that on Robust04, we first split BM25's run file into 5 sets before generating features. Use the following commands to generate features for BM25 ranking results on TREC-DL 19 and 20, as well as Robust04:

# TREC-DL 19
python -u ./rlt/features.py \
--output_path ./datasets/msmarco-v1-passage/features/ \
--run_path ./datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-bm25-1000.txt \
--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
--seq_len 1000 \
--mode infer 

# TREC-DL 20
python -u ./rlt/document_features.py \
--output_path ./datasets/msmarco-v1-passage/features/ \
--run_path ./datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-bm25-1000.txt \
--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
--seq_len 1000 \
--mode infer 

# Robust04
python -u ./process_robust04.py \
--mode split_run \
--run_path ./datasets/robust04/runs/robust04.run-title-bm25-1000.txt

fold_ids=("1" "2" "3" "4" "5")
for fold_id in "${fold_ids[@]}"
do
	python -u ./rlt/features.py \
	--output_path ./datasets/robust04/features/ \
	--run_path ./datasets/robust04/runs/robust04-fold${fold_id}.run-title-bm25-1000.txt \
	--qrels_path ./datasets/robust04/qrels/robust04.qrels.txt \
	--seq_len 1000 \
	--mode infer 
done

2.4.4 Generate features for SPLADE++ ranking results

Use the following commands to generate features for SPLADE++ ranking results on TREC-DL 19 and 20:

# TREC-DL 19
python -u ./rlt/features.py \
--output_path ./datasets/msmarco-v1-passage/features/ \
--run_path ./datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-splade-pp-ed-pytorch-1000.txt \
--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
--seq_len 1000 \
--mode infer 

# TREC-DL 20
python -u ./rlt/features.py \
--output_path ./datasets/msmarco-v1-passage/features/ \
--run_path ./datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-splade-pp-ed-pytorch-1000.txt \
--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
--seq_len 1000 \
--mode infer

2.4.5 Generate features for RepLLaMA ranking results

Use the following commands to generate features for RepLLaMA ranking results on TREC-DL 19 and 20:

# TREC-DL 19
python -u ./rlt/features.py \
--output_path ./datasets/msmarco-v1-passage/features/ \
--run_path ./datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-repllama-1000.txt \
--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
--seq_len 1000 \
--mode infer 

# TREC-DL 20
python -u ./rlt/features.py \
--output_path ./datasets/msmarco-v1-passage/features/ \
--run_path ./datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-repllama-1000.txt \
--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
--seq_len 1000 \
--mode infer

Note that the supervised RLT method LeCut needs to be fed with the query-item embeddings from the given neural retriever. We need to fetch embeddings from RepLLaMA and merge the embeddings with the features generated in the above step. We recommend using GPU to execute the following commands:

# TREC-DL 19
python -u ./rlt/embedding.py \
--feature_path ./datasets/msmarco-v1-passage/features/dl-19-passage.feature-original-repllama-1000 \
--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
--output_path ./datasets/msmarco-v1-passage/features/ \
--encoder repllama \
--split dl19 \
--query_path Tevatron/msmarco-passage \
--index_path Tevatron/msmarco-passage-corpus \
--fp16 \
--q_max_len=512 \
--p_max_len=512

# TREC-DL 20
python -u ./rlt/embedding.py \
--feature_path ./datasets/msmarco-v1-passage/features/dl-20-passage.feature-original-repllama-1000 \
--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
--output_path ./datasets/msmarco-v1-passage/features/ \
--encoder repllama \
--split dl20 \
--query_path Tevatron/msmarco-passage \
--index_path Tevatron/msmarco-passage-corpus \
--fp16 \
--q_max_len=512 \
--p_max_len=512

2.5 Training label generation

RLT methods (especially supervised ones) need the re-ranking quality in terms of a specific IR evaluation metric across all re-ranking cut-off candidates. However, only considering the re-ranking quality would ignore efficiency. Thus, to quantify different effectiveness/efficiency trade-offs in re-ranking, we use the efficiency-effectiveness trade-off (EET) metric values to score all re-ranking cut-off candidates; each re-ranking cut-off candidate would have a different score under each effectiveness/efficiency trade-off specified by EET.

EET has two hypeparamters, i.e., α and β. We consider α=-0.001, and β=0 (only effectiveness), 1 (balance effectiveness and efficiency) and 2 (more efficiency).

For the target IR valuation metric, we use follow Craswell et al., 2019 and Craswell et al., 2020 to use nDCG@10 on TREC-DL 19 and 20, and follow Dai et al., 2019 to use nDCG@10 on Robust04.

Similar to the above section, we generate 5 sets of training labels on Robust04 to enable 5-fold cross validation.

Please first create the folder where label files would be produced.

mkdir datasets/msmarco-v1-passage/labels
mkdir datasets/robust04/labels

2.5.1 BM25--RankLLaMA

Use the following commands to generate the training labels on TREC-DL 19 and 20, as well as Robust04:

# TREC-DL 19
python -u ./rlt/reranking_labels.py \
--retrieval_run_path datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-bm25-1000.txt \
--reranking_run_path datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-bm25-1000-rankllama-1000.txt \
--qrels_path datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
--metric ndcg@10 \
--seq_len 1000 \
--output_path datasets/msmarco-v1-passage/labels

# TREC-DL 20 
python -u ./rlt/reranking_labels.py \
--retrieval_run_path datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-bm25-1000.txt \
--reranking_run_path datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-bm25-1000-rankllama-1000.txt \
--qrels_path datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
--metric ndcg@10 \
--seq_len 1000 \
--output_path datasets/msmarco-v1-passage/labels

# Robust04
python -u ./process_robust04.py \
--mode split_run \
--run_path ./datasets/robust04/runs/robust04.run-title-bm25-1000-rankllama-doc-2048-1000.txt

fold_ids=("1" "2" "3" "4" "5")
for fold_id in "${fold_ids[@]}"
do
	python -u ./rlt/reranking_labels.py \
	--retrieval_run_path datasets/robust04/runs/robust04-fold${fold_id}.run-title-bm25-1000.txt \
	--reranking_run_path datasets/robust04/runs/robust04-fold${fold_id}.run-title-bm25-1000-rankllama-doc-2048-1000.txt \
	--qrels_path datasets/robust04/qrels/robust04.qrels.txt \
	--metric ndcg@20 \
	--seq_len 1000 \
	--output_path datasets/robust04/labels
done

2.5.2 SPLADE++--RankLLaMA

Use the following commands to generate the training labels on TREC-DL 19 and 20:

# TREC-DL 19
python -u ./rlt/reranking_labels.py \
--retrieval_run_path datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-splade-pp-ed-pytorch-1000.txt \
--reranking_run_path datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-splade-pp-ed-pytorch-1000-rankllama-1000.txt \
--qrels_path datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
--metric ndcg@10 \
--seq_len 1000 \
--output_path datasets/msmarco-v1-passage/labels

# TREC-DL 20
python -u ./rlt/reranking_labels.py \
--retrieval_run_path datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-splade-pp-ed-pytorch-1000.txt \
--reranking_run_path datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-splade-pp-ed-pytorch-1000-rankllama-1000.txt \
--qrels_path datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
--metric ndcg@10 \
--seq_len 1000 \
--output_path datasets/msmarco-v1-passage/labels

2.5.3 RepLLaMA--RankLLaMA

Use the following commands to generate the training labels on TREC-DL 19 and 20:

# TREC-DL 19
python -u ./rlt/reranking_labels.py \
--retrieval_run_path datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-repllama-1000.txt \
--reranking_run_path datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-repllama-1000-rankllama-1000.txt \
--qrels_path datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
--metric ndcg@10 \
--seq_len 1000 \
--output_path datasets/msmarco-v1-passage/labels

# TREC-DL 20
python -u ./rlt/reranking_labels.py \
--retrieval_run_path datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-repllama-1000.txt \
--reranking_run_path datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-repllama-1000-rankllama-1000.txt \
--qrels_path datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
--metric ndcg@10 \
--seq_len 1000 \
--output_path datasets/msmarco-v1-passage/labels

2.5.4 BM25--MonoT5

Use the following commands to generate the training labels on TREC-DL 19 and 20, as well as Robust04:

# TREC-DL 19
python -u ./rlt/reranking_labels.py \
--retrieval_run_path datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-bm25-1000.txt \
--reranking_run_path datasets/msmarco-v1-passage/runs/dl-19-passage.run-original-bm25-1000-monot5-1000.txt \
--qrels_path datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
--metric ndcg@10 \
--seq_len 1000 \
--output_path datasets/msmarco-v1-passage/labels

# TREC-DL 20
python -u ./rlt/reranking_labels.py \
--retrieval_run_path datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-bm25-1000.txt \
--reranking_run_path datasets/msmarco-v1-passage/runs/dl-20-passage.run-original-bm25-1000-monot5-1000.txt \
--qrels_path datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
--metric ndcg@10 \
--seq_len 1000 \
--output_path datasets/msmarco-v1-passage/labels

# Robust04
python -u ./process_robust04.py \
--mode split_run \
--run_path ./datasets/robust04/runs/robust04.run-title-bm25-1000-monot5-1000.txt

fold_ids=("1" "2" "3" "4" "5")
for fold_id in "${fold_ids[@]}"
do
	python -u ./rlt/reranking_labels.py \
	--retrieval_run_path datasets/robust04/runs/robust04-fold${fold_id}.run-title-bm25-1000.txt \
	--reranking_run_path datasets/robust04/runs/robust04-fold${fold_id}.run-title-bm25-1000-monot5-1000.txt \
	--qrels_path datasets/robust04/qrels/robust04.qrels.txt \
	--metric ndcg@20 \
	--seq_len 1000 \
	--output_path datasets/robust04/labels
done

3. Reproducing results

This section can easily reproduce all results for RQ1, RQ2 and RQ3 in the paper.

Methods that require a training set are trained on TREC-DL 19 and then inferred on TREC-DL 20, and vice versa.

Note that on Robust04, we employ 5-fold cross-validation. Please be aware that for Robust04, the RLT literature usually randomly divides the dataset into a training set (80% queries) and a test set (20% queries); however, different data division will impact the results. So we perform 5-fold cross-validation to eliminate the impact of the random data division on results. Next, for each test set, we need to generate corresponding training sets comprising features and labels, e.g., if fold 1 is used for testing, folds 2, 3, 4 and 5 would be used for training. Use the following commands to generate sets of features and labels for each test fold:

# generating features for training
python -u ./process_robust04.py \
--mode merge \
--fold_one_path ./datasets/robust04/features/robust04-fold1.feature-title-bm25-1000.json

# generate labels for training
metrics=("monot5-1000-ndcg@20" "monot5-1000-ndcg@20-eet-alpha-0.001-beta0" "monot5-1000-ndcg@20-eet-alpha-0.001-beta1" "monot5-1000-ndcg@20-eet-alpha-0.001-beta2" "rankllama-doc-2048-1000-ndcg@20" "rankllama-doc-2048-1000-ndcg@20-eet-alpha-0.001-beta0" "rankllama-doc-2048-1000-ndcg@20-eet-alpha-0.001-beta1" "rankllama-doc-2048-1000-ndcg@20-eet-alpha-0.001-beta2")

for metric in "${metrics[@]}"
do
	python -u ./process_robust04.py \
	--mode merge \
	--fold_one_path ./datasets/robust04/labels/robust04-fold1.label-title-bm25-1000.${metric}.json
done

All checkpoints would be stored in the ./checkpoint directory. Inference outputs would be stored in the ./output/{dataset name}.{retriever name} directory; an output file; each file has the same number of lines as queries in the test set; each line is composed of "query id\tpredicted cut-off".

3.1 Unsupervised RLT methods

We consider 3 unsupervised methods, i.e., Fixed-k, Greedy-k, Suprise. We also consider Oracle here.

3.1.1 Fixed-k

Run the following commands to perform Fixed-k on TREC-DL 19 and 20, as well as Robust04:

retrievers=("original-bm25-1000" "original-splade-pp-ed-pytorch-1000" "original-repllama-1000")

# TREC-DL 19
for retriever in "${retrievers[@]}"
do
python -u ./rlt/unsupervised_rlt.py \
--name fixed \
--feature_path ./datasets/msmarco-v1-passage/features/dl-19-passage.feature-${retriever}.json \
--output_path ./output
done

# TREC-DL 20
for retriever in "${retrievers[@]}"
do
python -u ./rlt/unsupervised_rlt.py \
--name fixed \
--feature_path ./datasets/msmarco-v1-passage/features/dl-20-passage.feature-${retriever}.json \
--output_path ./output
done

# Robust04
retrievers=("title-bm25-1000")
folds=("1" "2" "3" "4" "5")
for retriever in "${retrievers[@]}"
do
	for fold in "${folds[@]}"
	do
	python -u ./rlt/unsupervised_rlt.py \
	--name fixed \
	--feature_path ./datasets/robust04/features/robust04-fold${fold}.feature-${retriever}.json \
	--output_path ./output
	done
done

3.1.2 Greedy-k

Run the following commands to perform Greedy-k on TREC-DL 19 and 20, as well as Robust04:

retrievers=("original-bm25-1000" "original-splade-pp-ed-pytorch-1000" "original-repllama-1000")
metrics=("rankllama-1000-ndcg@10-eet-alpha-0.001-beta0" "rankllama-1000-ndcg@10-eet-alpha-0.001-beta1" "rankllama-1000-ndcg@10-eet-alpha-0.001-beta2" "monot5-1000-ndcg@10-eet-alpha-0.001-beta0" "monot5-1000-ndcg@10-eet-alpha-0.001-beta1" "monot5-1000-ndcg@10-eet-alpha-0.001-beta2")

# TREC-DL 19
for retriever in "${retrievers[@]}"
do
	for metric in "${metrics[@]}"
	do
	python -u ./rlt/unsupervised_rlt.py \
	--name greedy \
	--feature_path ./datasets/msmarco-v1-passage/features/dl-19-passage.feature-${retriever}.json \
	--train_labels_path ./datasets/msmarco-v1-passage/labels/dl-20-passage.label-${retriever}.${metric}.json \
	--output_path ./output
	done	
done

# TREC-DL 20
for retriever in "${retrievers[@]}"
do
	for metric in "${metrics[@]}"
	do
	python -u ./rlt/unsupervised_rlt.py \
	--name greedy \
	--feature_path ./datasets/msmarco-v1-passage/features/dl-20-passage.feature-${retriever}.json \
	--train_labels_path ./datasets/msmarco-v1-passage/labels/dl-19-passage.label-${retriever}.${metric}.json \
	--output_path ./output
	done	
done

# Robust04
retrievers=("title-bm25-1000")
metrics=("monot5-1000-ndcg@20" "monot5-1000-ndcg@20-eet-alpha-0.001-beta0" "monot5-1000-ndcg@20-eet-alpha-0.001-beta1" "monot5-1000-ndcg@20-eet-alpha-0.001-beta2" "rankllama-doc-2048-1000-ndcg@20" "rankllama-doc-2048-1000-ndcg@20-eet-alpha-0.001-beta0" "rankllama-doc-2048-1000-ndcg@20-eet-alpha-0.001-beta1" "rankllama-doc-2048-1000-ndcg@20-eet-alpha-0.001-beta2")
folds_training=("2345" "1345" "1245" "1235" "1234")
folds_inference=("1" "2" "3" "4" "5")

# train a model
for retriever in "${retrievers[@]}"
do
	for metric in "${metrics[@]}"
	do
		for ((i=0; i<${#folds_training[@]}; i++));
		do
		python -u ./rlt/unsupervised_rlt.py \
		--name greedy \
		--feature_path ./datasets/robust04/features/robust04-fold${folds_inference[$i]}.feature-${retriever}.json \
		--train_labels_path ./datasets/robust04/labels/robust04-fold${folds_training[$i]}.label-${retriever}.${metric}.json \
		--output_path ./output
		done
	done
done

3.1.3 Surprise

Note that Surprise only depends on retrieval scores and uses a score threshold to truncate a ranked list; Suprise cannot be tuned for re-rankers because the score threshold is set based on Cramer-von-Mises statistic testings and the threshold is not a tunable hyperparameter. Run the following commands to perform Surprise on TREC-DL 19 and 20, as well as Robust04:

retrievers=("original-bm25-1000" "original-splade-pp-ed-pytorch-1000" "original-repllama-1000")

# TREC-DL 19
for retriever in "${retrievers[@]}"
do
python -u ./rlt/unsupervised_rlt.py \
--name surprise \
--feature_path ./datasets/msmarco-v1-passage/features/dl-19-passage.feature-${retriever}.json \
--output_path ./output
done

# TREC-DL 20
for retriever in "${retrievers[@]}"
do
python -u ./rlt/unsupervised_rlt.py \
--name surprise \
--feature_path ./datasets/msmarco-v1-passage/features/dl-20-passage.feature-${retriever}.json \
--output_path ./output
done

# Robust04
retrievers=("title-bm25-1000")
folds=("1" "2" "3" "4" "5")
for retriever in "${retrievers[@]}"
do
	for fold in "${folds[@]}"
	do
	python -u ./rlt/unsupervised_rlt.py \
	--name surprise \
	--feature_path ./datasets/robust04/features/robust04-fold${fold}.feature-${retriever}.json \
	--output_path ./output
	done
done

3.1.4 Oracle

Run the following commands to perform Oracle on TREC-DL 19 and 20, as well as Robust04:

retrievers=("original-bm25-1000" "original-splade-pp-ed-pytorch-1000" "original-repllama-1000")
metrics=("rankllama-1000-ndcg@10" "monot5-1000-ndcg@10")

# TREC-DL 19
for retriever in "${retrievers[@]}"
do
	for metric in "${metrics[@]}"
	do
	python -u ./rlt/unsupervised_rlt.py \
	--name oracle \
	--test_labels_path ./datasets/msmarco-v1-passage/labels/dl-19-passage.label-${retriever}.${metric}.json \
	--output_path ./output
	done
done

# TREC-DL 20
for retriever in "${retrievers[@]}"
do
	for metric in "${metrics[@]}"
	do
	python -u ./rlt/unsupervised_rlt.py \
	--name oracle \
	--test_labels_path ./datasets/msmarco-v1-passage/labels/dl-20-passage.label-${retriever}.${metric}.json \
	--output_path ./output
	done
done

# Robust04
retrievers=("title-bm25-1000")
metrics=("monot5-1000-ndcg@20" "rankllama-doc-2048-1000-ndcg@20")
folds=("1" "2" "3" "4" "5")

for retriever in "${retrievers[@]}"
do
	for metric in "${metrics[@]}"
	do
		for fold in "${folds[@]}"
		do
		python -u ./rlt/unsupervised_rlt.py \
		--name oracle \
		--test_labels_path ./datasets/robust04/labels/robust04-fold${fold}.label-${retriever}.${metric}.json \
		--output_path ./output
		done
	done
done

3.2. Supervised RLT methods

We consider 5 supervised methods, i.e., BiCut, Choppy, AttnCut, MtCut and LeCut.

We recommend using GPU to execute all commands in this section.

3.2.1 Train and infer BiCut

Note that the training of BiCut is independent of re-ranking. As shown in our paper, BiCut uses a hyperparameter "η" to control trade-offs between effectiveness and efficiency. Run the following commands to train BiCut on TREC-DL 19 (TREC-DL 20) and then infer it on TREC-DL 20 (TREC-DL 19), and train and infer BiCut on Robust04 in a 5-fold cross-validation manner:

retrievers=("original-bm25-1000" "original-splade-pp-ed-pytorch-1000" "original-repllama-1000")
alphas=(0.4 0.5 0.6) # the symbol "alpha" used here corresponds to "η" as denoted in the paper.

# train a model on TREC-DL 19, and infer it on TREC-DL 20
for retriever in "${retrievers[@]}"
do
	for alpha in "${alphas[@]}"
	do 
	# training
	python -u ./rlt/supervised_rlt/main.py \
	--name bicut \
	--checkpoint_path ./checkpoint/ \
	--feature_path ./datasets/msmarco-v1-passage/features/dl-19-passage.feature-${retriever}.json \
	--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
	--epoch_num 100 \
	--alpha ${alpha} \
	--interval 1 \
	--seq_len 1000 \
	--batch_size 64 \
	--binarise_qrels \

	#inference
	python -u ./rlt/supervised_rlt/main.py \
	--name bicut \
	--checkpoint_path ./checkpoint/ \
	--feature_path ./datasets/msmarco-v1-passage/features/dl-20-passage.feature-${retriever}.json \
	--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
	--epoch_num 100 \
	--alpha ${alpha} \
	--interval 1 \
	--seq_len 1000 \
	--batch_size 64 \
	--binarise_qrels \
	--checkpoint_name dl-19-passage.${retriever}.bicut.alpha${alpha} \
	--output_path ./output \
	--infer
	done
done

# train a model on TREC-DL 20, and infer it on TREC-DL 19
for retriever in "${retrievers[@]}"
do
	for alpha in "${alphas[@]}"
	do 
	# training
	python -u ./rlt/supervised_rlt/main.py \
	--name bicut \
	--checkpoint_path ./checkpoint/ \
	--feature_path ./datasets/msmarco-v1-passage/features/dl-20-passage.feature-${retriever}.json \
	--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
	--epoch_num 100 \
	--alpha ${alpha} \
	--interval 1 \
	--seq_len 1000 \
	--batch_size 64 \
	--binarise_qrels \

	# inference
	python -u ./rlt/supervised_rlt/main.py \
	--name bicut \
	--checkpoint_path ./checkpoint/ \
	--feature_path ./datasets/msmarco-v1-passage/features/dl-19-passage.feature-${retriever}.json \
	--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
	--epoch_num 100 \
	--alpha ${alpha} \
	--interval 1 \
	--seq_len 1000 \
	--batch_size 64 \
	--binarise_qrels \
	--checkpoint_name dl-20-passage.${retriever}.bicut.alpha${alpha} \
	--output_path ./output \
	--infer
	done
done

# Robust04
retrievers=("title-bm25-1000")
alphas=(0.4 0.5 0.6) # the symbol "alpha" used here corresponds to "η" as denoted in the paper.
folds_training=("2345" "1345" "1245" "1235" "1234")
folds_inference=("1" "2" "3" "4" "5")

# train a model
for retriever in "${retrievers[@]}"
do
	for alpha in "${alphas[@]}"
	do
		for ((i=0; i<${#folds_training[@]}; i++));
		do
		# training
		python -u ./rlt/supervised_rlt/main.py \
		--name bicut \
		--checkpoint_path ./checkpoint/ \
		--feature_path ./datasets/robust04/features/robust04-fold${folds_training[$i]}.feature-${retriever}.json \
		--qrels_path ./datasets/robust04/qrels/robust04.qrels.txt \
		--epoch_num 100 \
		--alpha ${alpha} \
		--interval 1 \
		--seq_len 1000 \
		--batch_size 64 \

		#inference
		python -u ./rlt/supervised_rlt/main.py \
		--name bicut \
		--checkpoint_path ./checkpoint/ \
		--feature_path ./datasets/robust04/features/robust04-fold${folds_inference[$i]}.feature-${retriever}.json \
		--qrels_path ./datasets/robust04/qrels/robust04.qrels.txt \
		--epoch_num 100 \
		--alpha ${alpha} \
		--interval 1 \
		--seq_len 1000 \
		--batch_size 64 \
		--checkpoint_name robust04-fold${folds_training[$i]}.${retriever}.bicut.alpha${alpha} \
		--output_path ./output \
		--infer
		done
	done
done

3.2.2 Train and infer Choppy, AttnCut and MtCut

Run the following commands to train Choppy, AttnCut and MtCut on TREC-DL 19 (TREC-DL 20) and then infer them on TREC-DL 20 (TREC-DL 19), and train and infer them on Robust04 in a 5-fold cross-validation manner:

retrievers=("original-bm25-1000" "original-splade-pp-ed-pytorch-1000" "original-repllama-1000" )
metrics=("rankllama-1000-ndcg@10-eet-alpha-0.001-beta0" "rankllama-1000-ndcg@10-eet-alpha-0.001-beta1" "rankllama-1000-ndcg@10-eet-alpha-0.001-beta2" "monot5-1000-ndcg@10-eet-alpha-0.001-beta0" "monot5-1000-ndcg@10-eet-alpha-0.001-beta1" "monot5-1000-ndcg@10-eet-alpha-0.001-beta2")
models=("choppy" "attncut" "mmoecut")

# train a model on TREC-DL 19, and infer it on TREC-DL 20
for retriever in "${retrievers[@]}"
do
	for metric in "${metrics[@]}"
	do
		for model in "${models[@]}"
		do
		# training
		python -u ./rlt/supervised_rlt/main.py \
		--name ${model} \
		--checkpoint_path ./checkpoint/ \
		--feature_path ./datasets/msmarco-v1-passage/features/dl-19-passage.feature-${retriever}.json \
		--label_path ./datasets/msmarco-v1-passage/labels/dl-19-passage.label-${retriever}.${metric}.json \
		--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
		--epoch_num 100 \
		--interval 1 \
		--seq_len 1000 \
		--batch_size 64 \
		--binarise_qrels \

		# inference
		python -u ./rlt/supervised_rlt/main.py \
		--name ${model} \
		--checkpoint_path ./checkpoint/ \
		--feature_path ./datasets/msmarco-v1-passage/features/dl-20-passage.feature-${retriever}.json \
		--label_path ./datasets/msmarco-v1-passage/labels/dl-20-passage.label-${retriever}.${metric}.json \
		--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
		--epoch_num 100 \
		--interval 1 \
		--seq_len 1000 \
		--batch_size 64 \
		--binarise_qrels \
		--checkpoint_name dl-19-passage.${retriever}.${model}.${metric} \
		--output_path ./output \
		--infer

		done
	done
done

# train a model on TREC-DL 20, and infer it on TREC-DL 19
for retriever in "${retrievers[@]}"
do
	for metric in "${metrics[@]}"
	do 
		for model in "${models[@]}"
		do
		# training
		python -u ./rlt/supervised_rlt/main.py \
		--name ${model} \
		--checkpoint_path ./checkpoint/ \
		--feature_path ./datasets/msmarco-v1-passage/features/dl-20-passage.feature-${retriever}.json \
		--label_path ./datasets/msmarco-v1-passage/labels/dl-20-passage.label-${retriever}.${metric}.json \
		--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
		--epoch_num 100 \
		--interval 1 \
		--seq_len 1000 \
		--batch_size 64 \
		--binarise_qrels \
	
		# inference
		python -u ./rlt/supervised_rlt/main.py \
		--name ${model} \
		--checkpoint_path ./checkpoint/ \
		--feature_path ./datasets/msmarco-v1-passage/features/dl-19-passage.feature-${retriever}.json \
		--label_path ./datasets/msmarco-v1-passage/labels/dl-19-passage.label-${retriever}.${metric}.json \
		--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
		--epoch_num 100 \
		--interval 1 \
		--seq_len 1000 \
		--batch_size 64 \
		--binarise_qrels \
		--checkpoint_name dl-20-passage.${retriever}.${model}.${metric} \
		--output_path ./output \
		--infer
		done
	done
done

# Robust04
retrievers=("title-bm25-1000")
metrics=("rankllama-doc-2048-1000-ndcg@20-eet-alpha-0.001-beta0" "rankllama-doc-2048-1000-ndcg@20-eet-alpha-0.001-beta1" "rankllama-doc-2048-1000-ndcg@20-eet-alpha-0.001-beta2" "monot5-1000-ndcg@20-eet-alpha-0.001-beta0" "monot5-1000-ndcg@20-eet-alpha-0.001-beta1" "monot5-1000-ndcg@20-eet-alpha-0.001-beta2")
models=("choppy" "attncut" "mmoecut")
folds_training=("2345" "1345" "1245" "1235" "1234")
folds_inference=("1" "2" "3" "4" "5")

for retriever in "${retrievers[@]}"
do
	for metric in "${metrics[@]}"
	do 
		for model in "${models[@]}"
		do
			for ((i=0; i<${#folds_training[@]}; i++));
			do
			# training
			python -u ./rlt/supervised_rlt/main.py \
			--name ${model} \
			--checkpoint_path ./checkpoint/ \
			--feature_path ./datasets/robust04/features/robust04-fold${folds_training[$i]}.feature-${retriever}.json \
			--label_path ./datasets/robust04/labels/robust04-fold${folds_training[$i]}.label-${retriever}.${metric}.json \
			--qrels_path ./datasets/robust04/qrels/robust04.qrels.txt \
			--epoch_num 100 \
			--interval 1 \
			--seq_len 1000 \
			--batch_size 64 \
	
			# inference
			python -u ./rlt/supervised_rlt/main.py \
			--name ${model} \
			--checkpoint_path ./checkpoint/ \
			--feature_path ./datasets/robust04/features/robust04-fold${folds_inference[$i]}.feature-${retriever}.json \
			--label_path ./datasets/robust04/labels/robust04-fold${folds_inference[$i]}.label-${retriever}.${metric}.json \
			--qrels_path ./datasets/robust04/qrels/robust04.qrels.txt \
			--epoch_num 100 \
			--interval 1 \
			--seq_len 1000 \
			--batch_size 64 \
			--checkpoint_name robust04-fold${folds_training[$i]}.${retriever}.${model}.${metric} \
			--output_path ./output \
			--infer
			done
		done
	done
done

3.2.3 Train and infer LeCut

Note that LeCut can only work for RepLLaMA. Run the following commands to train LeCut on TREC-DL 19 (TREC-DL 20) and then infer it on TREC-DL 20 (TREC-DL 19):

retrievers=("original-repllama-1000")
metrics=("rankllama-1000-ndcg@10-eet-alpha-0.001-beta0" "rankllama-1000-ndcg@10-eet-alpha-0.001-beta1" "rankllama-1000-ndcg@10-eet-alpha-0.001-beta2")
models=("lecut")

# train a model on TREC-DL 19, and infer it on TREC-DL 20
for retriever in "${retrievers[@]}"
do
	for metric in "${metrics[@]}"
	do 
		for model in "${models[@]}"
		do
		# training
		python -u ./rlt/supervised_rlt/main.py \
		--name ${model} \
		--checkpoint_path ./checkpoint/ \
		--feature_path ./datasets/msmarco-v1-passage/features/dl-19-passage.feature-${retriever}.embed-repllama.json \
		--label_path ./datasets/msmarco-v1-passage/labels/dl-19-passage.label-${retriever}.${metric}.json \
		--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
		--epoch_num 100 \
		--interval 1 \
		--seq_len 1000 \
		--batch_size 64 \
		--binarise_qrels \
	
		# inference
		python -u ./rlt/supervised_rlt/main.py \
		--name ${model} \
		--checkpoint_path ./checkpoint/ \
		--feature_path ./datasets/msmarco-v1-passage/features/dl-20-passage.feature-${retriever}.embed-repllama.json \
		--label_path ./datasets/msmarco-v1-passage/labels/dl-20-passage.label-${retriever}.${metric}.json \
		--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
		--epoch_num 100 \
		--interval 1 \
		--seq_len 1000 \
		--batch_size 64 \
		--binarise_qrels \
		--checkpoint_name dl-19-passage.${retriever}.${model}-embed-repllama.${metric} \
		--output_path ./output \
		--infer
		done
	done
done

# train a model on TREC-DL 20, and infer it on TREC-DL 19
for retriever in "${retrievers[@]}"
do
	for metric in "${metrics[@]}"
	do 
		for model in "${models[@]}"
		do
		# training
		python -u ./rlt/supervised_rlt/main.py \
		--name ${model} \
		--checkpoint_path ./checkpoint/ \
		--feature_path ./datasets/msmarco-v1-passage/features/dl-20-passage.feature-${retriever}.embed-repllama.json \
		--label_path ./datasets/msmarco-v1-passage/labels/dl-20-passage.label-${retriever}.${metric}.json \
		--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-20-passage.qrels.txt \
		--epoch_num 100 \
		--interval 1 \
		--seq_len 1000 \
		--batch_size 64 \
		--binarise_qrels \
	
		# inference
		python -u ./rlt/supervised_rlt/main.py \
		--name ${model} \
		--checkpoint_path ./checkpoint/ \
		--feature_path ./datasets/msmarco-v1-passage/features/dl-19-passage.feature-${retriever}.embed-repllama.json \
		--label_path ./datasets/msmarco-v1-passage/labels/dl-19-passage.label-${retriever}.${metric}.json \
		--qrels_path ./datasets/msmarco-v1-passage/qrels/dl-19-passage.qrels.txt \
		--epoch_num 100 \
		--interval 1 \
		--seq_len 1000 \
		--batch_size 64 \
		--binarise_qrels \
		--checkpoint_name dl-20-passage.${retriever}.${model}-embed-repllama.${metric} \
		--output_path ./output \
		--infer
		done
	done
done

3.3 Evaluation

A file that shows the results (e.g., average predicted cut-offs, and re-ranking results using the predicted cut-offs) would be generated in the corresponding `./output/{dataset name}.{retriever name}" directory.

Note that for Robust04, we already get the prediction on each fold, we need to merge all folds into one file before performing the evaluation. Please run the following commands to merge predictions:

mkdir ./output/robust04.title-bm25-1000

python -u ./process_robust04.py \
--mode merge_k \
--fold_one_pattern './output/robust04-fold1.title-bm25-1000/robust04-fold1.*'

Use the following commands to evaluate RLT methods w.r.t the pipeline of BM25--RankLLaMA:

# TREC-DL 19
python -u ./rlt/evaluation.py \
--pattern './output/dl-19-passage.original-bm25-1000/dl-19-passage.original-bm25-1000.*' \
--reranking_labels_path ./datasets/msmarco-v1-passage/labels/dl-19-passage.label-original-bm25-1000.rankllama-1000-ndcg@10.json \

# TREC-DL 20
python -u ./rlt/evaluation.py \
--pattern './output/dl-20-passage.original-bm25-1000/dl-20-passage.original-bm25-1000.*' \
--reranking_labels_path ./datasets/msmarco-v1-passage/labels/dl-20-passage.label-original-bm25-1000.rankllama-1000-ndcg@10.json \

# Robust04
python -u ./rlt/evaluation.py \
--pattern './output/robust04.title-bm25-1000/robust04.*' \
--reranking_labels_path ./datasets/robust04/labels/robust04.label-title-bm25-1000.rankllama-doc-2048-1000-ndcg@20.json

Use the following commands to evaluate RLT methods w.r.t the pipeline of SPLADE++--RankLLaMA:

# TREC-DL 19
python -u ./rlt/evaluation.py \
--pattern './output/dl-19-passage.original-splade-pp-ed-pytorch-1000/dl-19-passage.original-splade-pp-ed-pytorch-1000.*' \
--reranking_labels_path ./datasets/msmarco-v1-passage/labels/dl-19-passage.label-original-splade-pp-ed-pytorch-1000.rankllama-1000-ndcg@10.json \

# TREC-DL 20
python -u ./rlt/evaluation.py \
--pattern './output/dl-20-passage.original-splade-pp-ed-pytorch-1000/dl-20-passage.original-splade-pp-ed-pytorch-1000.*' \
--reranking_labels_path ./datasets/msmarco-v1-passage/labels/dl-20-passage.label-original-splade-pp-ed-pytorch-1000.rankllama-1000-ndcg@10.json \

Use the following commands to evaluate RLT methods w.r.t the pipeline of RepLLaMA--RankLLaMA:

# TREC-DL 19
python -u ./rlt/evaluation.py \
--pattern './output/dl-19-passage.original-repllama-1000/dl-19-passage.original-repllama-1000.*' \
--reranking_labels_path ./datasets/msmarco-v1-passage/labels/dl-19-passage.label-original-repllama-1000.rankllama-1000-ndcg@10.json \

# TREC-DL 20
python -u ./rlt/evaluation.py \
--pattern './output/dl-20-passage.original-repllama-1000/dl-20-passage.original-repllama-1000.*' \
--reranking_labels_path ./datasets/msmarco-v1-passage/labels/dl-20-passage.label-original-repllama-1000.rankllama-1000-ndcg@10.json \

Use the following commands to evaluate RLT methods w.r.t the pipeline of BM25--MonoT5:

# TREC-DL 19
python -u ./rlt/evaluation.py \
--pattern './output/dl-19-passage.original-bm25-1000/dl-19-passage.original-bm25-1000.*' \
--reranking_labels_path ./datasets/msmarco-v1-passage/labels/dl-19-passage.label-original-bm25-1000.monot5-1000-ndcg@10.json \

# TREC-DL 20
python -u ./rlt/evaluation.py \
--pattern './output/dl-20-passage.original-bm25-1000/dl-20-passage.original-bm25-1000.*' \
--reranking_labels_path ./datasets/msmarco-v1-passage/labels/dl-20-passage.label-original-bm25-1000.monot5-1000-ndcg@10.json \

# Robust04
python -u ./rlt/evaluation.py \
--pattern './output/robust04.title-bm25-1000/robust04.*' \
--reranking_labels_path ./datasets/robust04/labels/robust04.label-title-bm25-1000.monot5-1000-ndcg@20.json

4. Reproducing plots

Run plots.ipynb can recreate all plots represented in the paper. The recreated plots would be stored in the ./plots directory.

5. Results on Robust04

Due to limited space in our paper, we present results on Robust04 here. Table 1 and Table 2 show the results of RLT methods in predicting re-ranking cut-off points for the BM25--RankLLaMA and BM25--MonoT5 pipelines, respectively. Surprisingly, MonoT5 generally performs better than RankLLaMA on Robust04. We have three main observations for RLT methods.

First, the unsupervised method, Fixed-k (100 or 200) already strikes a good balance between re-ranking effectiveness and efficiency compared to other methods. Also, deeper re-ranking depths, e.g., Fixed-k (1000), do not necessarily yield better results, resulting in a waste of computational resources.

Second, supervised methods yield slightly superior re-ranking effectiveness but come with an increased re-ranking cost compared to Fixed-k (100 or 200). Similar to the findings reported in the paper, MtCut, a supervised method that jointly learns RLT with other tasks, demonstrates improved re-ranking results. For example, without considering Oracle, for BM25--RankLLaMA, MtCut (β=2) achieves the highest re-ranking result (0.469) with an average re-ranking depth of 125.71. Similarly, for BM25–MonoT5, MtCut (β=0) attains the highest re-ranking result (0.560) with an average re-ranking depth of 754.96.

Thirdly, Oracle surpasses all RLT methods in re-ranking effectiveness with limited re-ranking cost; it underscores the need to propose new RLT methods for re-ranking for future work.

Table 1: A comparison of RLT methods, optimized for re-ranking effectiveness/efficiency tradeoffs, in predicting re-ranking cut-off points for the BM25--RankLLaMA pipeline on Robust04.

Method Avg. k (cost) nDCG@20
w/o re-ranking - 0.413
Fixed-k (10) 10 0.430
Fixed-k (20) 20 0.435
Fixed-k (100) 100 0.467
Fixed-k (200) 200 0.465
Fixed-k (500) 500 0.453
Fixed-k (1000) 1000 0.451
Surprise 721.91 0.449
Greedy-k (β=0) 398.85 0.455
BiCut (η=0.40) 341.05 0.461
Choppy (β=0) 495.03 0.455
AttnCut (β=0) 771.01 0.452
MtCut (β=0) 590.67 0.457
Greedy-k (β=1) 136.62 0.468
BiCut (η=0.50) 243.79 0.463
Choppy (β=1) 480.02 0.456
AttnCut (β=1) 237.37 0.462
MtCut (β=1) 223.32 0.464
Greedy-k (β=2) 121.34 0.468
BiCut (η=0.60) 166.22 0.464
Choppy (β=2) 487.69 0.453
AttnCut (β=2) 121.27 0.465
MtCut (β=2) 125.71 0.469
Oracle 131.42 0.559

Table 2: A comparison of RLT methods, optimized for re-ranking effectiveness/efficiency tradeoffs, in predicting re-ranking cut-off points for the BM25--MonoT5 pipeline on Robust04.

Method Avg. k (cost) nDCG@20
w/o re-ranking - 0.413
Fixed-k (10) 10 0.440
Fixed-k (20) 20 0.452
Fixed-k (100) 100 0.543
Fixed-k (200) 200 0.556
Fixed-k (500) 500 0.556
Fixed-k (1000) 1000 0.556
Surprise 721.91 0.556
Greedy-k (β=0) 795.41 0.556
BiCut (η=0.40) 341.05 0.555
Choppy (β=0) 489.28 0.538
AttnCut (β=0) 799.28 0.560
MtCut (β=0) 754.96 0.560
Greedy-k (β=1) 209.23 0.556
BiCut (η=0.50) 243.79 0.557
Choppy (β=1) 512.17 0.555
AttnCut (β=1) 261.03 0.554
MtCut (β=1) 266.57 0.558
Greedy-k (β=2) 142.37 0.545
BiCut (η=0.60) 166.22 0.549
Choppy (β=2) 484.45 0.550
AttnCut (β=2) 131.48 0.539
MtCut (β=2) 147.32 0.544
Oracle 212.35 0.635

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