# Improving Health Question Answering with Reliable and Time-Aware Evidence Retrieval arXiv: https://arxiv.org/abs/2404.08359 ยท HTML: https://mlsanity.com/paper/2404.08359/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 53 references that are in the map. - First posted on arXiv 12 Apr 2024. ## Builds on (most cited first, 24 of 24) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020) - [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022) - [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018) - [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020) - [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019) - [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021) - [The Power of Noise: Redefining Retrieval for RAG Systems](https://mlsanity.com/paper/2401.14887/) (2024) - [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021) - [AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web](https://mlsanity.com/paper/2305.13117/) (2023) - [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018) - [HEAD-QA: A Healthcare Dataset for Complex Reasoning](https://mlsanity.com/paper/1906.04701/) (2019) - [SciFact-Open: Towards open-domain scientific claim verification](https://mlsanity.com/paper/2210.13777/) (2022) - [QED: A Framework and Dataset for Explanations in Question Answering](https://mlsanity.com/paper/2009.06354/) (2020) - [Scientific Fact-Checking: A Survey of Resources and Approaches](https://mlsanity.com/paper/2305.16859/) (2023) - [SemEval-2023 Task 11: Learning With Disagreements (LeWiDi)](https://mlsanity.com/paper/2304.14803/) (2023) - [A Survey on Machine Reading Comprehension Systems](https://mlsanity.com/paper/2001.01582/) (2020) - [A Review on Fact Extraction and Verification](https://mlsanity.com/paper/2010.03001/) (2020) - [Results of the seventh edition of the BioASQ Challenge](https://mlsanity.com/paper/2006.09174/) (2020) - [Interpreting Predictive Probabilities: Model Confidence or Human Label Variation?](https://mlsanity.com/paper/2402.16102/) (2024) - [Modeling Information Change in Science Communication with Semantically Matched Paraphrases](https://mlsanity.com/paper/2210.13001/) (2022) - [Time-Aware Evidence Ranking for Fact-Checking](https://mlsanity.com/paper/2009.06402/) (2020) - [Comparing Knowledge Sources for Open-Domain Scientific Claim Verification](https://mlsanity.com/paper/2402.02844/) (2024) - [Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports](https://mlsanity.com/paper/2304.13180/) (2023) ## Built on by (most cited first, 12 of 12) - [A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?](https://mlsanity.com/paper/2503.24235/) (2025) - [It's High Time: A Survey of Temporal Question Answering](https://mlsanity.com/paper/2505.20243/) (2025) - [MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering](https://mlsanity.com/paper/2406.05845/) (2024) - [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025) - [The Next Phase of Scientific Fact-Checking: Advanced Evidence Retrieval from Complex Structured Academic Papers](https://mlsanity.com/paper/2506.20844/) (2025) - [On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2502.14759/) (2025) - [Combining Evidence and Reasoning for Biomedical Fact-Checking](https://mlsanity.com/paper/2509.13879/) (2025) - [Continually Self-Improving Language Models for Bariatric Surgery Question--Answering](https://mlsanity.com/paper/2505.16102/) (2025) - [Combating Biomedical Misinformation through Multi-modal Claim Detection and Evidence-based Verification](https://mlsanity.com/paper/2509.13888/) (2025) - [Enhancing Answer Attribution for Faithful Text Generation with Large Language Models](https://mlsanity.com/paper/2410.17112/) (2024) - [MedSEBA: Synthesizing Evidence-Based Answers Grounded in Evolving Medical Literature](https://mlsanity.com/paper/2509.00414/) (2025) - [Evaluating and Improving Evidence-Grounded Fact-Checking in LLMs via Multi-Round Evidence Ablation](https://mlsanity.com/paper/2609.08943/) (2026) ## Abstract (arXiv) In today's digital world, seeking answers to health questions on the Internet is a common practice. However, existing question answering (QA) systems often rely on using pre-selected and annotated evidence documents, thus making them inadequate for addressing novel questions. Our study focuses on the open-domain QA setting, where the key challenge is to first uncover relevant evidence in large knowledge bases. By utilizing the common retrieve-then-read QA pipeline and PubMed as a trustworthy collection of medical research documents, we answer health questions from three diverse datasets. We modify different retrieval settings to observe their influence on the QA pipeline's performance, including the number of retrieved documents, sentence selection process, the publication year of articles, and their number of citations. Our results reveal that cutting down on the amount of retrieved documents and favoring more recent and highly cited documents can improve the final macro F1 score up to 10%. We discuss the results, highlight interesting examples, and outline challenges for future research, like managing evidence disagreement and crafting user-friendly explanations.