AmbigDocs: Reasoning across Documents on Different Entities under the Same Name

[Paper] [Homepage] [Dataset]

Introduction

This is the repository for the paper AmbigDocs: Reasoning across Documents on Different Entities under the Same Name.

We introduce AmbigDocs, a benchmark for testing the abilities of current LMs to distinguish confusing entity mentions and generate a cohesive answer. Single instance consists of a question asking about an ambiguous entity and a list of gold document-answer pairs for each disambiguated entity.

Dataset Contents

Download the data from here and place under src/data. Additionally, we use the Wikipedia snapshot from December 20th, 2018. Please place the documents (psgs_w100.tsv) in same directory, which can be downloaded from DPR repo.

Each data instance consists of question, ambiguous_entity, qid, and a list of documents. Each element in documents consists of title which is a disambiguated entity, text, pid for referencing psgs_w100.tsv, and answer.

Setup

pip install -r requirements.txt

For evaluation, please place the necessary LMs under src/models. For generation, please place question_converter-3b, t5_xxl_true_nli_mixture under src/models.

Dataset Generation

For dataset generation, please refer to src/generation subdirectory.

Evaluation

1. Executing below will run inference on test split. mode represents the following: 1: Gold Only, 2: Gold+Retrieved, 3: Retrieved Only, 4: Few-shot Put the name of the model you are using in model. If this contains "gpt", put openAPI key afterwards. Otherwise, put the model path to the argument.

   python qa.py [data_path] [mode] [model] [openAPI key/path_to_QA_model]
   

2. Executing below will compute preliminary operations for computing Disambig-F1 score.

   sh df1.sh [mode] [model]
   

3. Executing below will compute Answer Recall / Entity Recall / Entity-Answer Recall / Disambig-F1 scores.

   python eval.py [mode] [model]
   

While our study mainly focuses on Gold Only setting, we also experiment on retrieved corpus. We leverage GTR as our retriever and the codes taken from ALCE repo. Please download necessary pre-computed embeddings and GTR model and execute retrieval.py.

python retrieval.py [path_to_gtr_wikipedia_index.pkl] [path_to_GTR_model] \
--retriever gtr \
--data_file ../../../data/test.json \
--output_file ../../../data/test_retrieved.json

Citations

If you find our work helpful, please cite us as

@article{lee2024ambigdocs,
    title={AmbigDocs: Reasoning across Documents on Different Entities under the Same Name},
    author={Lee, Yoonsang and Ye, Xi and Choi, Eunsol},
    journal={arXiv preprint arXiv:2404.12447},
    year={2024}
}