mlsanityData to 7 Oct 2026

Agent-based Automated Claim Matching with Instruction-following LLMs

arXiv 2510.23924 ↗First posted 27 Oct 2025cs.CL cs.AIcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

We present a novel agent-based approach for the automated claim matching task with instruction-following LLMs. We propose a two-step pipeline that first generates prompts with LLMs, to then perform claim matching as a binary classification task with LLMs. We demonstrate that LLM-generated prompts can outperform SOTA with human-generated prompts, and that smaller LLMs can do as well as larger ones in the generation process, allowing to save computational resources. We also demonstrate the effectiveness of using different LLMs for each step of the pipeline, i.e. using an LLM for prompt generation, and another for claim matching. Our investigation into the prompt generation process in turn reveals insights into the LLMs' understanding of claim matching.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 23 of its 37 references that are in the map. First posted on arXiv 27 Oct 2025.

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23 of 37 references.

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  1. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate30 May

Other references · 22

  1. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  2. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  3. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  4. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  5. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
  6. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 724
  7. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 599
  8. 2021A Survey on Automated Fact-Checkingcited by 376
  9. 2021True Few-Shot Learning with Language Modelscited by 343
  10. 2020That is a Known Lie: Detecting Previously Fact-Checked Claimscited by 82
  11. 2024A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learningcited by 74
  12. 2021Automated Fact-Checking: A Surveycited by 55
10 more
  1. 2020Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Mediacited by 46
  2. 2021Claim Matching Beyond English to Scale Global Fact-Checkingcited by 42
  3. 2024Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Researchcited by 35
  4. 2023Multilingual Previously Fact-Checked Claim Retrievalcited by 32
  5. 2024FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMscited by 27
  6. 2023Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformationcited by 18
  7. 2021The Role of Context in Detecting Previously Fact-Checked Claimscited by 17
  8. 2022Matching Tweets With Applicable Fact-Checks Across Languagescited by 15
  9. 2024Just rephrase it! Uncertainty estimation in closed-source language models via multiple rephrased queriescited by 15
  10. 2025Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checkingcited by 12

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