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Code for "DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation" (Arxiv)

If you find our paper or code useful, please cite the paper:

@inproceedings{xie2024doclens,
      title={DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation}, 
      author={Yiqing Xie and Sheng Zhang and Hao Cheng and Pengfei Liu and Zelalem Gero and Cliff Wong and Tristan Naumann and Hoifung Poon and Carolyn Rose},
      year={2024},
      booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics}
}

 

Data

To evaluate with DocLens, you will need two json files:

• (1) a file with the input and reference, which should be put under data/, and
• (2) a file with the generated text, which should be put under results/

The file with the input and reference is a list of dicts. Each dict represents a test example and is in the following format:

{
    "example_id": the id of this example,
    "input": the input text,
    "reference": the reference output # Optional, required for claim recall/precision evaluation
}

Note that the reference key is required for the claim recall/precision evaluation, but is not required for citation recall/precision evaluation.

The file with the generated text is also a list of dicts with a similar format:

{
    "example_id": the id of this example,
    "input": the input text,
    "output": the system output 
}

 

Evaluation with DocLens

We provide the code to compute claim recall, claim precision, citation recall, and citation precision.

Claim Generation

To evaluate claim recall and claim precision, we will need to first generate the subclaims for the reference and outputs by running:

bash scripts/eval_general_claim_generation.sh $SAVENAME $REFERENCE $PROMPT_FILE

$SAVENAME is the name of the file for generated text without the '.json' file extension (e.g., if your file is results/generation.json, we have $SAVENAME="generation"). $REFERENCE is the name of the file with the input and reference without the '.json' file extension (e.g., if your file is data/reference.json, we have $REFERENCE="reference"). $PROMPT_FILE is the prompt for claim extraction. We provide a simple prompt template in claim_evaluation/prompts/general_subclaim_generation.json. You can also create your own prompt file.

 

Claim Recall and Claim Precision Computation

After generating the claims, we can compute claim recall and claim precision. You can use the GPT-4 evaluator by running:

bash scripts/eval_general_api_claim_entailment.sh $SAVENAME $REFERENCE $PROMPT_FILE

We have $PROMPT_FILE="claim_evaluation/prompts/general_claim_entail.json" by default

You can also use the Mistral or TRUE evaluators:

bash scripts/eval_general_model_claim_entailment.sh $SAVENAME $REFERENCE $EVAL_MODEL $PROMPT_FILE

You can choose the evaluator model by setting $EVAL_MODEL=TRUE or $EVAL_MODEL=Mistral. If you want to use Mistral for evaluation, you can also specify the $PROMPT_FILE, which is by default claim_evaluation/prompts/general_claim_entail_Mistral.json

 

Citation Recall and Citation Precision Computation

The computation of citation recall and citation precision do not need reference. You can use GPT-4 to compute citation recall and precision:

bash scripts/eval_general_api_citation.sh $SAVENAME $PROMPT_FILE

We have $PROMPT_FILE="citation_evaluation/prompts/general_citation_entail.json" by default.

You can also use the Mistral or TRUE evaluators:

bash scripts/eval_general_model_citation.sh $SAVENAME $EVAL_MODEL $PROMPT_FILE

We have $PROMPT_FILE="citation_evaluation/prompts/general_citation_entail_Mistral.json" by default.

 

Aggregate Scores

The scores of all examples can be aggregated by aggregate_scores.py. For example:

python aggregate_scores.py --result_file results/${SAVENAME}.json \
    --eval_claim_recall \       # compute claim recall
    --eval_claim_precision \    # compute claim precision
    --eval_citations \          # compute citation recall or citation precision
    --eval_model GPT            # can also be Mistral or TRUE, depend on the evaluator model you used

 

Reproduce the Results in our Paper

Here are the instructions for reproducing the results on ACI-BENCH (note generation), MIMIC (report summarization), and MeQSum (question summarization) in our paper.

Data

We provide the preprocessed datafiles as follows:

data
├── ACI-Bench-TestSet-1_clean.claim_min1max30.json  # data of ACI-BENCH-test1 with generated reference claims
├── ACI-Bench-TestSet-1_clean.json                  # data of ACI-BENCH-test1
├── meqsum-test_clean.json                          # data of MeQSum-test
├── mimic-sampled200_clean.json                     # data of MIMIC (the 200 test examples sampled by proportion of different splits)
└── mimic-sampled200_clean.claim_min1max30.json     # data of MIMIC (200 samples) with generated reference claims

The .claim_min1max30.json files contain the reference subclaims we generated.

 

Run Medical Text Generation

To run text generation, you'll need to call the run.py file. This will follow the instructions in the prompt file and generate a piece of text based on the input text of each example.

We provide several example scripts unser scripts/ named run_DATASET.sh or run_DATASET_0shot.sh. For example,

bash scripts/run_mimic.sh $CONFIG_FILE

We provide several example config files under configs/ Note that for ACI-BENCH, we provide scripts for both full-note generation (e.g., scripts/run_acibench_full.sh) and per-section generation (e.g., scripts/run_acibench_persection.sh).

 

Evaluation with DocLens

The scripts for evaluation are similar to evaluating your own text. For example:

bash scripts/eval_mimic_api.sh $SAVENAME

and

bash scripts/eval_mimic_model.sh $SAVENAME $EVAL_MODEL