| Challenge: | Automated evaluation of natural language generation tasks fails to focus on medical QA because of the diversity in medical terminology. |
| Approach: | They propose a new data structure, imap, to capture key information in questions and answers. |
| Outcome: | The proposed model outperforms state-of-the-art metrics in correlation with human scores. |
Similar Papers
MedScore: Generalizable Factuality Evaluation of Open-ended Long-form Medical Answers by Domain-adapted Claim Decomposition and Verification (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing factuality evaluation pipelines are poor matches for medical domains . existing methods are limited to objective, entity-centric, formulaic texts . |
| Approach: | They propose a pipeline to decompose medical answers into condition-aware valid facts . they use a decomposition-then-verify approach to evaluate generated text . |
| Outcome: | The proposed method extracts up to three times as many valid facts as existing methods . the resulting factuality score substantially varies by decomposition method, corpus, and used backbone LLM . |
Can Large Language Models Accurately Generate Answer Keys for Health-related Questions? (2025.acl-short)
Copied to clipboard
| Challenge: | Evaluating the factuality of LLM generated answers is challenging for many tasks, including question answering. |
| Approach: | They propose to use information nuggets to evaluate the factuality of LLM generated answers . they find providing an example and extracting nuggots from an answer is the best approach . |
| Outcome: | The proposed model performs best when compared to human nugget generation. |
INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to evaluate the quality of language generation do not provide explicit explanation of their verdicts. |
| Approach: | They propose a fine-grained explainable evaluation metric for text generation that harnesses human instruction and implicit knowledge of GPT-4 to fine-tune it. |
| Outcome: | The proposed model outperforms all other unsupervised metrics on translation, captioning, data-to-text, and commonsense generation tasks. |
AutoMedEval: Harnessing Language Models for Automatic Medical Capability Evaluation (2025.acl-long)
Copied to clipboard
Xiechi Zhang, Zetian Ouyang, Linlin Wang, Gerard De Melo, Zhu Cao, Xiaoling Wang, Ya Zhang, Yanfeng Wang, Liang He
| Challenge: | Existing evaluation methods based on large language models (LLMs) are expensive and lack expertise due to limitations in human expertise. |
| Approach: | They propose an open-source automatic evaluation model with 13B parameters specifically engineered to measure the question-answering proficiency of medical LLMs. |
| Outcome: | The proposed model surpasses baselines in terms of correlation with human judgments. |
DocLens: Multi-aspect Fine-grained Medical Text Evaluation (2024.acl-long)
Copied to clipboard
Yiqing Xie, Sheng Zhang, Hao Cheng, Pengfei Liu, Zelalem Gero, Cliff Wong, Tristan Naumann, Hoifung Poon, Carolyn Rose
| Challenge: | Medical text generation systems are widely used to assist with administrative work and highlight salient information to support decision-making. |
| Approach: | They propose a set of metrics to evaluate completeness, conciseness, and attribution of medical text at a fine-grained level. |
| Outcome: | The proposed framework exhibits substantially higher agreement with medical experts than existing metrics. |
LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation (2025.findings-emnlp)
Copied to clipboard
Ming Zhang, Yujiong Shen, Zelin Li, Huayu Sha, Binze Hu, Yuhui Wang, Chenhao Huang, Shichun Liu, Jingqi Tong, Changhao Jiang, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Current medical benchmarks have limitations in question design, data sources and evaluation methods. |
| Approach: | They propose a new benchmark covering five core medical areas . it includes 2,996 questions created from real-world electronic health records . |
| Outcome: | The proposed model covers five core medical areas and includes 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios. |
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing learning metrics are limited to tasks where large human ratings are available. |
| Approach: | They propose a model-based natural language generation (NLG) evaluation metric that is highly correlated with human judgements without requiring human annotation. |
| Outcome: | The proposed metric outperforms all prior unsupervised metrics on multiple NLG tasks including translation, image captioning, and WebNLG text generation. |
Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review (2025.findings-acl)
Copied to clipboard
| Challenge: | Evidence-based medicine (EBM) is at the forefront of modern healthcare, emphasizing the use of the best available scientific evidence to guide clinical decisions. |
| Approach: | They propose to investigate the use of Natural Language Processing (NLP) techniques to identify, appraise, synthesize, apply, and disseminate evidence in EBM. |
| Outcome: | The proposed methods support the five fundamental steps of EBM—Ask, Acquire, Appraise, Apply, and Assess. |
An Investigation of Evaluation Methods in Automatic Medical Note Generation (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent studies show that doctors can save significant amounts of time when using automatic note generation. |
| Approach: | They propose task-specific metrics for automatic note generation from medical conversation summarization and generation, including knowledge-graph embedding-based metrics, customized model-based measures with domain-specific weights, and ensemble metrics. |
| Outcome: | The proposed evaluation metrics are compared to existing models and can have different behaviors on different types of clinical notes datasets. |
From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations (2025.emnlp-main)
Copied to clipboard
Benlu Wang, Iris Xia, Yifan Zhang, Junda Wang, null Feiyun Ouyang, Shuo Han, Arman Cohan, Hong Yu, Zonghai Yao
| Challenge: | Existing benchmarks assess only the final answer with a wide numerical tolerance, overlooking systematic reasoning failures and potentially causing serious clinical misjudgments. |
| Approach: | They propose a new step-by-step evaluation pipeline that assesses formula selection, entity extraction, and arithmetic computation. |
| Outcome: | The proposed method improves the accuracy of large language models on medical benchmarks from 16.35% to 53.19%. |