Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques

• URL: http://arxiv.org/abs/2506.05924v1
• Date: Fri, 06 Jun 2025 09:46:09 GMT
• Title: Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques
• Authors: Xiaofei Xu, Xiuzhen Zhang, Ke Deng,
• Abstract summary: MisMitiFact is an efficient framework for generating fact-grounded counter-responses at scale.<n>We develop lightweight, fine-grained critique models trained on data sourced from readily available fact-checking sites.<n>It achieves 5x increase in feedback generation throughput, making it highly suitable for cost-effective, large-scale misinformation mitigation.
• Score: 9.514892000592912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fake news and misinformation poses a significant threat to society, making efficient mitigation essential. However, manual fact-checking is costly and lacks scalability. Large Language Models (LLMs) offer promise in automating counter-response generation to mitigate misinformation, but a critical challenge lies in their tendency to hallucinate non-factual information. Existing models mainly rely on LLM self-feedback to reduce hallucination, but this approach is computationally expensive. In this paper, we propose MisMitiFact, Misinformation Mitigation grounded in Facts, an efficient framework for generating fact-grounded counter-responses at scale. MisMitiFact generates simple critique feedback to refine LLM outputs, ensuring responses are grounded in evidence. We develop lightweight, fine-grained critique models trained on data sourced from readily available fact-checking sites to identify and correct errors in key elements such as numerals, entities, and topics in LLM generations. Experiments show that MisMitiFact generates counter-responses of comparable quality to LLMs' self-feedback while using significantly smaller critique models. Importantly, it achieves ~5x increase in feedback generation throughput, making it highly suitable for cost-effective, large-scale misinformation mitigation. Code and LLM prompt templates are at https://github.com/xxfwin/MisMitiFact.

Related papers

• Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers [59.168391398830515]
  We evaluate 12 pre-trained LLMs and one specialized fact-verifier, using a collection of examples from 14 fact-checking benchmarks.<n>We highlight the importance of addressing annotation errors and ambiguity in datasets.<n> frontier LLMs with few-shot in-context examples, often overlooked in previous works, achieve top-tier performance.
  arXiv  Detail & Related papers  (2025-06-16T10:32:10Z)
• Unraveling Misinformation Propagation in LLM Reasoning [19.89817963822589]
  We show how misinformation propagates within Large Language Models' reasoning process.<n>Applying factual corrections early in the reasoning process most effectively reduces misinformation propagation.<n>Our work offers a practical approach to mitigating misinformation propagation.
  arXiv  Detail & Related papers  (2025-05-24T06:45:45Z)
• Balancing Truthfulness and Informativeness with Uncertainty-Aware Instruction Fine-Tuning [79.48839334040197]
  Instruction fine-tuning (IFT) can increase the informativeness of large language models (LLMs), but may reduce their truthfulness.<n>In this paper, we empirically demonstrate how unfamiliar knowledge in IFT datasets can negatively affect the truthfulness of LLMs.<n>We introduce two new IFT paradigms, $UNIT_cut$ and $UNIT_ref$, to address this issue.
  arXiv  Detail & Related papers  (2025-02-17T16:10:30Z)
• From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning [91.79567270986901]
  Large Language Models (LLMs) tend to prioritize adherence to user prompts over providing veracious responses.<n>Recent works propose to employ supervised fine-tuning (SFT) to mitigate the sycophancy issue.<n>We propose a novel supervised pinpoint tuning (SPT), where the region-of-interest modules are tuned for a given objective.
  arXiv  Detail & Related papers  (2024-09-03T07:01:37Z)
• CaLM: Contrasting Large and Small Language Models to Verify Grounded Generation [76.31621715032558]
  Grounded generation aims to equip language models (LMs) with the ability to produce more credible and accountable responses. We introduce CaLM, a novel verification framework. Our framework empowers smaller LMs, which rely less on parametric memory, to validate the output of larger LMs.
  arXiv  Detail & Related papers  (2024-06-08T06:04:55Z)
• Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs [30.179703001666173]
  Factuality issue is a critical concern for Large Language Models (LLMs) We propose GraphEval to evaluate an LLM's performance using a substantially large test dataset. Test dataset is retrieved from a large knowledge graph with more than 10 million facts without expensive human efforts.
  arXiv  Detail & Related papers  (2024-04-01T06:01:17Z)
• Fighting Fire with Fire: Adversarial Prompting to Generate a Misinformation Detection Dataset [10.860133543817659]
  We propose an LLM-based approach of creating silver-standard ground-truth datasets for identifying misinformation. Specifically speaking, given a trusted news article, our proposed approach involves prompting LLMs to automatically generate a summarised version of the original article. To investigate the usefulness of this dataset, we conduct a set of experiments where we train a range of supervised models for the task of misinformation detection.
  arXiv  Detail & Related papers  (2024-01-09T10:38:13Z)
• LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback [65.84061725174269]
  Recent large language models (LLM) are leveraging human feedback to improve their generation quality. We propose LLMRefine, an inference time optimization method to refine LLM's output. We conduct experiments on three text generation tasks, including machine translation, long-form question answering (QA), and topical summarization. LLMRefine consistently outperforms all baseline approaches, achieving improvements up to 1.7 MetricX points on translation tasks, 8.1 ROUGE-L on ASQA, 2.2 ROUGE-L on topical summarization.
  arXiv  Detail & Related papers  (2023-11-15T19:52:11Z)
• On the Risk of Misinformation Pollution with Large Language Models [127.1107824751703]
  We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of Open-Domain Question Answering (ODQA) systems.
  arXiv  Detail & Related papers  (2023-05-23T04:10:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.