mlsanityData to 7 Oct 2026

LLM-based Corroborating and Refuting Evidence Retrieval for Scientific Claim Verification

arXiv 2503.07937 ↗First posted 11 Mar 2025cs.AIcited by 5

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

In this paper, we introduce CIBER (Claim Investigation Based on Evidence Retrieval), an extension of the Retrieval-Augmented Generation (RAG) framework designed to identify corroborating and refuting documents as evidence for scientific claim verification. CIBER addresses the inherent uncertainty in Large Language Models (LLMs) by evaluating response consistency across diverse interrogation probes. By focusing on the behavioral analysis of LLMs without requiring access to their internal information, CIBER is applicable to both white-box and black-box models. Furthermore, CIBER operates in an unsupervised manner, enabling easy generalization across various scientific domains. Comprehensive evaluations conducted using LLMs with varying levels of linguistic proficiency reveal CIBER's superior performance compared to conventional RAG approaches. These findings not only highlight the effectiveness of CIBER but also provide valuable insights for future advancements in LLM-based scientific claim verification.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 14 of its 35 references that are in the map. First posted on arXiv 11 Mar 2025.

Builds on

14 of 35 references.

References · 14

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  3. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  4. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  5. 2019MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claimscited by 169
  6. 2023Knowledge Graph Prompting for Multi-Document Question Answeringcited by 150
  7. 2020Generating Fact Checking Explanationscited by 132
  8. 2018DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learningcited by 100
  9. 2019A Richly Annotated Corpus for Different Tasks in Automated Fact-Checkingcited by 74
  10. 2022FiD-Light: Efficient and Effective Retrieval-Augmented Text Generationcited by 63
  11. 2023Large Language Model based Long-tail Query Rewriting in Taobao Searchcited by 61
  12. 2023Trapping LLM Hallucinations Using Tagged Context Promptscited by 34
2 more
  1. 2023From Classification to Generation: Insights into Crosslingual Retrieval Augmented ICLcited by 17
  2. 2024RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbotscited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026NOVA: NOise-aware Verbal Confidence CAlibration for Robust Large Language Models in RAG Systemscited by 5
  2. 2026FactReview: Evidence-Grounded Peer Review with Execution-Based Claim Verificationcited by 2
  3. 2026Group Perspective Matters: Regulating Debate Relationships Can Mitigate Blind Conformity in Multi-Agent Debatecited by 0
  4. 2026PURPOSE: Poisoning Conflict Resolution in RAG via Proxy-Fact-Grounded Updatescited by 0
  5. 2026Medical Causal Hypothesis Verification with Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Can AI Validate Science? Benchmarking LLMs for Accurate Scientific Claim rightarrow Evidence Reasoning
  2. 2025Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs
  3. 2026Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking
  4. 2024Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments
  5. 2025Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs
  6. 2024Claim Verification in the Age of Large Language Models: A Survey
  7. 2026When Retrieval Helps and Distracts: Evaluating Evidence-Generating LLMs for Biomedical Claim Verification
  8. 2025Beyond Retrieval: Improving Evidence Quality for LLM-based Multimodal Fact-Checking