mlsanityData to 7 Oct 2026

Iterative Tree Analysis for Medical Critics

arXiv 2501.10642 ↗First posted 18 Jan 2025cs.CLcited by 0

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Abstract (arXiv)

Large Language Models (LLMs) have been widely adopted across various domains, yet their application in the medical field poses unique challenges, particularly concerning the generation of hallucinations. Hallucinations in open-ended long medical text manifest as misleading critical claims, which are difficult to verify due to two reasons. First, critical claims are often deeply entangled within the text and cannot be extracted based solely on surface-level presentation. Second, verifying these claims is challenging because surface-level token-based retrieval often lacks precise or specific evidence, leaving the claims unverifiable without deeper mechanism-based analysis. In this paper, we introduce a novel method termed Iterative Tree Analysis (ITA) for medical critics. ITA is designed to extract implicit claims from long medical texts and verify each claim through an iterative and adaptive tree-like reasoning process. This process involves a combination of top-down task decomposition and bottom-up evidence consolidation, enabling precise verification of complex medical claims through detailed mechanism-level reasoning. Our extensive experiments demonstrate that ITA significantly outperforms previous methods in detecting factual inaccuracies in complex medical text verification tasks by 10%. Additionally, we will release a comprehensive test set to the public, aiming to foster further advancements in research within this domain.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 33 of its 34 references that are in the map. First posted on arXiv 18 Jan 2025.

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References · 33

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  2. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,324
  3. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  4. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
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  6. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
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  8. 2024MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmarkcited by 362
  9. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  10. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 329
  11. 2023FreshLLMs: Refreshing Large Language Models with Search Engine Augmentationcited by 308
  12. 2024AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environmentscited by 215
21 more
  1. 2024Long-form factuality in large language modelscited by 146
  2. 2019A Question-Entailment Approach to Question Answeringcited by 133
  3. 2024AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulatorcited by 99
  4. 2022SciFact-Open: Towards open-domain scientific claim verificationcited by 94
  5. 2023Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Taskscited by 73
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  8. 2024DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Modelscited by 63
  9. 2023FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Modelscited by 44
  10. 2024Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approachcited by 40
  11. 2023Evaluating Hallucinations in Chinese Large Language Modelscited by 33
  12. 2024Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrievalcited by 28
  13. 2024Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMscited by 27
  14. 2024MedFuzz: Exploring the Robustness of Large Language Models in Medical Question Answeringcited by 26
  15. 2024HalluDial: A Large-Scale Benchmark for Automatic Dialogue-Level Hallucination Evaluationcited by 26
  16. 2024RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Modelscited by 24
  17. 2024From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyondcited by 24
  18. 2024RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Modelscited by 24
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  20. 2024RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answeringcited by 13
  21. 2024Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-Oasiscited by 8

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  2. 2025FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domain
  3. 2026Med-HEAL: Analyzing and Mitigating Hallucinations in Medical LLMs with Hallucination-Aware In-Context Learning
  4. 2026CuraView: A Multi-Agent Framework for Medical Hallucination Detection with GraphRAG-Enhanced Knowledge Verification
  5. 2024Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models
  6. 2026Hallucination Mitigating for Medical Report Generation
  7. 2026ClinHallu: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM Reasoning
  8. 2024Detecting and Evaluating Medical Hallucinations in Large Vision Language Models