ExpertQA : Expert-Curated Questions and Attributed Answers

Paper

Find the paper at https://arxiv.org/abs/2309.07852

Dataset

ExpertQA contains 2177 examples, which are validated on various axes of factuality and attribution. The main data can be found at

• data/r2_compiled_anon.jsonl

This can be loaded simply using the data loaders at data_utils as:

data = example_utils.read_examples("data/r2_compiled_anon.jsonl")

The file contains newline-separated json dictionaries with the following fields:

• question - Question written by an expert.
• annotator_id - Anonymized annotator ID of the author of the question.
• answers - Dict mapping model names to an Answer object. The model names can be one of {gpt4, bing_chat, rr_sphere_gpt4, rr_gs_gpt4, post_hoc_sphere_gpt4, post_hoc_gs_gpt4}.
• metadata - A dictionary with the following fields:
  • question_type - The question type(s) separated by "|".
  • field - The field to which the annotator belonged.
  • specific_field - More specific field name within the broader field.

Each Answer object contains the following fields:

• answer_string: The answer string.
• attribution: List of evidences for the answer (not linked to specific claims). Note that these are only URLs, the evidence passages are stored in the Claim object -- see below.
• claims: List of Claim objects for the answer.
• revised_answer_string: Revised answer by annotator.
• usefulness: Usefulness of original answer marked by annotator.
• annotation_time: Time taken for annotating this answer.
• annotator_id: Anonymized annotator ID of the person who validated this answer.

Each Claim object contains the following fields:

• claim_string: Original claim string.
• evidence: List of evidences for the claim (URL+passage or URL).
• support: Attribution marked by annotator.
• reason_missing_support: Reason for missing support specified by annotator.
• informativeness: Informativeness of claim for the question, marked by annotator.
• worthiness: Worthiness of citing claim marked by annotator.
• correctness: Factual correctness of claim marked by annotator.
• reliability: Reliability of source evidence marked by annotator.
• revised_claim: Revised claim by annotator.
• revised_evidence: Revised evidence by annotator.
• atomic_claims: Atomic claims for Fact score estimation.
• atomic_evidences: Atomic claim-evidences for Fact score estimation.
• fact_score: Fact score for each claim.
• autoais_label: Autoais label for the original claim and original evidence.

Additional Files

• We also provide the list of questions (2507 in total) collected in stage 1 of our annotation. These can be found at data/r1_data_anon.jsonl.
• Answers were sampled from different systems for the purpose of annotation. Files containing all answers from a specific system can be found at data/r1_data_answers_{MODEL_KEY}_claims_anon.jsonl.
• In the main dataset, evidences for each claim can be URL+passages OR only URLs, depending on which system the answer was sampled from. We provide all passage evidences in the file data/r2_compiled_all_evidences_autoais_anon.jsonl.

Long-form QA

The random and domain split for the long-form QA dataset can be found at data/lfqa/. The files for the random split are prefixed with rand_lfqa_ and the files for the domain split are prefixed with domain_lfqa_.

Modeling

Response collection

Found at modeling/response_collection. The scripts for collecting responses from different systems are at:

• bing_chat: fetch_bingchat_responses.py
• gpt4: fetch_openai_responses.py
• rr_gs_gpt4: retrieve_and_read.py
• rr_sphere_gpt4: sphere_and_read.py
• post_hoc_gs_gpt4: post_hoc_cite.py
• post_hoc_sphere_gpt4: post_hoc_cite_sphere.py

Attribution estimation

Found at modeling/auto_attribution.

• First, the script convert_for_autoais.py may be used to fetch textual evidences when URLs are returned as attributions.
• The script autoais.py can then be used to generate autoAIS predictions using the TRUE model.
• The evaluation scripts compute_autoais_score.py and compute_human_correlation.py compute averaged autoAIS scores, and correlations with reference judgements of attribution in our dataset.
• To finetune the TRUE model on the domain split of our dataset, use the script finetune_autoais.py.

Factuality estimation

Found at modeling/fact_score. See sample usage at get_fact_score.sh.

• First, we need to break down the claims in the dataset to atomic claims. This can be done with the script break_down_to_atomic_claims.py.
• Next, we need to retrieve evidence for all atomic claims. This can be done using retrieve_evidence_for_claims.py, which retrieves the top-5 passages from the top-10 Google search results, with each atomic claim as the query.
• Finally, we compute the scores using factscore.py, which prompts ChatGPT for the factuality of each atomic claim.
• The claim-level factuality scores and the averaged F1 scores can then be computed using compute_factuality_f1.py.

Long-form QA

Found at modeling/lfqa. Example usages at bash_scripts/run_lfqa.sh.

• The script convert_for_lfqa.py converts the split data into a format required for long-form QA training.
• For finetuning FlanT5-XXL, use run_gen_qa.py.
• For finetuning Llama-2-7B and Vicuna-7B, use run_sft_qa.py.

Evaluation

Scripts and documentation for running evaluation are in the eval/ directory.

• nlg_eval.py computes ROUGE and QAFactEval scores. Download the QAFactEval models from https://github.com/salesforce/QAFactEval and place them at qafacteval_models.

License

This project is licensed under the MIT License - see the LICENSE file for details

Citation

@inproceedings{malaviya2024expertqa,
title={Expert{QA}: Expert-Curated Questions and Attributed Answers},
author={Chaitanya Malaviya and Subin Lee and Sihao Chen and Elizabeth Sieber and Mark Yatskar and Dan Roth},
booktitle={2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
year={2024},
url={https://openreview.net/forum?id=hhC3nTgfOv}
}