From Passive Response to Proactive Correction: DEDUCE Enhances LLM Robustness Against Input Fact Perturbations
Score
71提出新问题、新方法、新数据集与指标，填补了输入事实扰动这一空白。
方法描述清晰，实验覆盖多个模型与基准，论证完整。
为真实场景中的错误输入过滤与纠错提供可落地方案。
最新预印本，紧跟大模型可靠性前沿。
arXiv 预印本，尚未经同行评审，但来源可追溯且数据充分。
Base 78.5 × T2 Source weight ×0.9 = 71
Curation line 70Selected
Why selected
Overall assessment: novel method, new dataset and metrics, with solid experiments across multiple model families.
AI commentary
「This paper deserves attention from AI product teams, because factual errors in user inputs are more common than expected, while most models simply follow the misleading premise. DEDUCE makes correction an explicit step rather than a post-hoc patch, which is especially valuable for customer service, search, and financial Q&A. If code and models are open-sourced later, the adoption barrier will be even lower.」
Quick overview
Large language models often produce confident but factually incorrect answers when user inputs contain misleading premises, a vulnerability the authors attribute to fact perturbations. Existing hallucination mitigation largely assumes reliable inputs and ignores this failure mode. This paper proposes DEDUCE, a three-stage framework that turns LLMs from passive responders into proactive correctors: it detects errors via fine-grained fact extraction and verification, devises correction strategies through multi-perspective deliberation, and then corrects misconceptions while delivering trustworthy answers. The authors also introduce MisFactQA, a dataset with varying degrees of factual errors, and new metrics for robustness evaluation. Experiments on TruthfulQA, FalseQA, and MisFactQA show that DEDUCE consistently improves accuracy and error-correction capability across Qwen, LLaMA, and Gemma families, demonstrating effectiveness and scalability.
- Identifies and defines 'input fact perturbation,' a blind spot in current hallucination mitigation.
- Proposes DEDUCE, a three-stage framework: detect errors, deliberate from multiple perspectives, and correct while delivering reliable answers.
- Releases MisFactQA dataset and new robustness evaluation metrics.
- Demonstrates significant and consistent gains across Qwen, LLaMA, and Gemma model families.
Full text
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