mlsanityData to 14 Oct 2026

Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims

arXiv 2506.10728 ↗First posted 12 Jun 2025cs.CL cs.IRcited by 3

On the map: Retrieval-augmented generation → Medical and biomedical RAG

Abstract (arXiv)

Claims made by individuals or entities are oftentimes nuanced and cannot be clearly labeled as entirely "true" or "false" -- as is frequently the case with scientific and political claims. However, a claim (e.g., "vaccine A is better than vaccine B") can be dissected into its integral aspects and sub-aspects (e.g., efficacy, safety, distribution), which are individually easier to validate. This enables a more comprehensive, structured response that provides a well-rounded perspective on a given problem while also allowing the reader to prioritize specific angles of interest within the claim (e.g., safety towards children). Thus, we propose ClaimSpect, a retrieval-augmented generation-based framework for automatically constructing a hierarchy of aspects typically considered when addressing a claim and enriching them with corpus-specific perspectives. This structure hierarchically partitions an input corpus to retrieve relevant segments, which assist in discovering new sub-aspects. Moreover, these segments enable the discovery of varying perspectives towards an aspect of the claim (e.g., support, neutral, or oppose) and their respective prevalence (e.g., "how many biomedical papers believe vaccine A is more transportable than B?"). We apply ClaimSpect to a wide variety of real-world scientific and political claims featured in our constructed dataset, showcasing its robustness and accuracy in deconstructing a nuanced claim and representing perspectives within a corpus. Through real-world case studies and human evaluation, we validate its effectiveness over multiple baselines.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 16 of its 33 references that are in the map. First posted on arXiv 12 Jun 2025.

Builds on

16 of 33 references.

References · 16

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  3. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  4. 2018DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learningcited by 100
  5. 2023The science of fake newscited by 97
  6. 2023Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Methodcited by 97
  7. 2023Stance Detection with Collaborative Role-Infused LLM-Based Agentscited by 53
  8. 2017Fully Automated Fact Checking Using External Sourcescited by 27
  9. 2024TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervisioncited by 25
  10. 2024Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examplescited by 24
  11. 2024Are Large Language Models a Good Replacement of Taxonomies?cited by 19
  12. 2023Prompting or Fine-tuning? A Comparative Study of Large Language Models for Taxonomy Constructioncited by 18
4 more
  1. 2022RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Mediacited by 13
  2. 2019Automatic Fact-Checking Using Context and Discourse Informationcited by 11
  3. 2024CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Promptscited by 10
  4. 2024A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansioncited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspirationcited by 4
  2. 2026EvoTaxo: Building and Evolving Taxonomy from Social Media Streamscited by 1
  3. 2026Reinforcing Agentic Creativity in Scientific Ideation with Night Sciencecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Bridging the Gap: Leveraging Retrieval-Augmented Generation to Better Understand Public Concerns about Vaccines
  2. 2026Facet-Level Tracing of Evidence Uncertainty and Hallucination in RAG
  3. 2026When Retrieval Helps and Distracts: Evaluating Evidence-Generating LLMs for Biomedical Claim Verification
  4. 2025Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain
  5. 2025Contradictions in Context: Challenges for Retrieval-Augmented Generation in Healthcare
  6. 2024Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments
  7. 2025Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals
  8. 2026MIRAGE: Defending Long-Form RAG Against Misinformation Pollution