# A Benchmark for Long-Form Medical Question Answering

arXiv: https://arxiv.org/abs/2411.09834 · HTML: https://mlsanity.com/paper/2411.09834/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 27 references that are in the map.
- First posted on arXiv 14 Nov 2024.

## Builds on (most cited first, 19 of 19)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [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)
- [MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://mlsanity.com/paper/2203.14371/) (2022)
- [Benchmarking Retrieval-Augmented Generation for Medicine](https://mlsanity.com/paper/2402.13178/) (2024)
- [BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://mlsanity.com/paper/2402.10373/) (2024)
- [MEDITRON-70B: Scaling Medical Pretraining for Large Language Models](https://mlsanity.com/paper/2311.16079/) (2023)
- [Capabilities of Gemini Models in Medicine](https://mlsanity.com/paper/2404.18416/) (2024)
- [WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild](https://mlsanity.com/paper/2406.04770/) (2024)
- [Do We Still Need Clinical Language Models?](https://mlsanity.com/paper/2302.08091/) (2023)
- [AlpaCare:Instruction-tuned Large Language Models for Medical Application](https://mlsanity.com/paper/2310.14558/) (2023)
- [MedExQA: Medical Question Answering Benchmark with Multiple Explanations](https://mlsanity.com/paper/2406.06331/) (2024)
- [KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques](https://mlsanity.com/paper/2403.05881/) (2024)
- [K-QA: A Real-World Medical Q&A Benchmark](https://mlsanity.com/paper/2401.14493/) (2024)
- [CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures](https://mlsanity.com/paper/2410.05235/) (2024)

## Built on by (most cited first, 25 of 25)

- [A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers](https://mlsanity.com/paper/2508.21148/) (2025)
- [CounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmarking of Large Language Models in Mental Health Question Answering](https://mlsanity.com/paper/2506.08584/) (2025)
- [3MDBench: Medical Multimodal Multi-agent Dialogue Benchmark](https://mlsanity.com/paper/2504.13861/) (2025)
- [WiCkeD: A Simple Method to Make Multiple Choice Benchmarks More Challenging](https://mlsanity.com/paper/2502.18316/) (2025)
- [MedGUIDE: Benchmarking Clinical Decision-Making in Large Language Models](https://mlsanity.com/paper/2505.11613/) (2025)
- [AgentLongBench: A Controllable Long Benchmark For Long-Contexts Agents via Environment Rollouts](https://mlsanity.com/paper/2601.20730/) (2026)
- [A Controllable Examination for Long-Context Language Models](https://mlsanity.com/paper/2506.02921/) (2025)
- [Judging Against the Reference: Uncovering Knowledge-Driven Failures in LLM-Judges on QA Evaluation](https://mlsanity.com/paper/2601.07506/) (2026)
- [MedAraBench: Large-Scale Arabic Medical Question Answering Dataset and Benchmark](https://mlsanity.com/paper/2602.01714/) (2026)
- [Tuning Language Models for Robust Prediction of Diverse User Behaviors](https://mlsanity.com/paper/2505.17682/) (2025)
- [ProMedical: Hierarchical Fine-Grained Criteria Modeling for Medical LLM Alignment via Explicit Injection](https://mlsanity.com/paper/2604.08326/) (2026)
- [Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuning](https://mlsanity.com/paper/2505.21958/) (2025)
- [PIE: Performance Interval Estimation for Free-Form Generation Tasks](https://mlsanity.com/paper/2509.07309/) (2025)
- [CQA-Eval: Designing Reliable Evaluations of Multi-paragraph Clinical QA under Resource Constraints](https://mlsanity.com/paper/2510.10415/) (2025)
- [How Uncertain Is the Grade? A Benchmark of Uncertainty Metrics for LLM-Based Automatic Assessment](https://mlsanity.com/paper/2602.16039/) (2026)
- [Revealing the Learning Dynamics of Long-Context Continual Pre-training](https://mlsanity.com/paper/2604.02650/) (2026)
- [PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian Language](https://mlsanity.com/paper/2505.18331/) (2025)
- [HIVMedQA: Benchmarking large language models for HIV medical decision support](https://mlsanity.com/paper/2507.18143/) (2025)
- [Investigating LLM Capabilities on Long Context Comprehension for Medical Question Answering](https://mlsanity.com/paper/2510.18691/) (2025)
- [Safer in Translation? Presupposition Robustness in Indic Languages](https://mlsanity.com/paper/2511.01360/) (2025)
- [Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making](https://mlsanity.com/paper/2602.11924/) (2026)
- [When Cases Get Rare: A Retrieval Benchmark for Off-Guideline Clinical Question Answering](https://mlsanity.com/paper/2605.21807/) (2026)
- [DrugBench: Evaluating AI Control Protocols for Medication Harm Mitigation](https://mlsanity.com/paper/2606.20663/) (2026)
- [The Consistency Dilemma in LLMs: Generator-Evaluator Agreement and Vulnerability to Mistakes](https://mlsanity.com/paper/2606.30653/) (2026)
- [Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering](https://mlsanity.com/paper/2607.06641/) (2026)

## Abstract (arXiv)

There is a lack of benchmarks for evaluating large language models (LLMs) in long-form medical question answering (QA). Most existing medical QA evaluation benchmarks focus on automatic metrics and multiple-choice questions. While valuable, these benchmarks fail to fully capture or assess the complexities of real-world clinical applications where LLMs are being deployed. Furthermore, existing studies on evaluating long-form answer generation in medical QA are primarily closed-source, lacking access to human medical expert annotations, which makes it difficult to reproduce results and enhance existing baselines. In this work, we introduce a new publicly available benchmark featuring real-world consumer medical questions with long-form answer evaluations annotated by medical doctors. We performed pairwise comparisons of responses from various open and closed-source medical and general-purpose LLMs based on criteria such as correctness, helpfulness, harmfulness, and bias. Additionally, we performed a comprehensive LLM-as-a-judge analysis to study the alignment between human judgments and LLMs. Our preliminary results highlight the strong potential of open LLMs in medical QA compared to leading closed models. Code & Data: https://github.com/lavita-ai/medical-eval-sphere