Would You Walk to the Car Wash? Revealing the Salience Bias of Large Language Models in Commonsense Reasoning
Score
78首次提出并系统量化显著性偏差，区分知识缺失与知识抑制，为常识推理研究提供了新视角和开源基准。
实验设计严谨，覆盖12个主流模型，并用对照实验验证核心假设，证据链完整。
提出的轻量级提示缓解方案无需重训即可应用，SaliTrap 基准可被社区直接用于模型评估。
发布及时，切中大模型部署中常见的误导性问题，兼顾前沿性与当下热点。
论文来自 arXiv 预印本，代码仓库已公开，研究方法和数据可复现，可信度高。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
It is the first to systematically define and quantify salience bias, while offering a high-recall recovery analysis and a simple, effective mitigation.
AI commentary
「This article pinpoints the 'trap' we often encounter when using LLMs: the model knows commonsense but is easily led astray by obvious distractors. Rather than merely exposing a flaw, it offers a low-cost fix—rephrasing the question can substantially improve performance. It is a valuable read for those working on prompt engineering or deploying models in practice.」
Quick overview
This paper introduces 'Salience Bias', a phenomenon where large language models (LLMs) in commonsense reasoning are hijacked by conspicuous irrelevant distractors (e.g., numerical values), causing them to ignore implicit physical or commonsense prerequisites. The authors construct the SaliTrap Benchmark, a high-quality dataset across four trap dimensions, and evaluate 12 state-of-the-art LLMs. They find that all mainstream models suffer significantly from salience bias, with severity scaling with distractor density, and that detecting the trap is often decoupled from avoiding it. Crucially, by re-eliciting models with the task framing stripped away, they show that over 90% of sycophantic-compliance failures stem from knowledge suppression rather than knowledge absence: the requisite commonsense is intrinsically present but actively crowded out by salient distractors. Lightweight inference-time prompting alone substantially closes the gap without retraining. The findings relocate the bottleneck of commonsense reasoning failures from model competence to elicitation, and SaliTrap is released as a testbed.
- Introduces 'Salience Bias': models are easily misled by conspicuous distractors and ignore implicit commonsense prerequisites.
- Builds the SaliTrap Benchmark with four trap dimensions; all 12 evaluated mainstream LLMs suffer significantly.
- Shows most failures stem from knowledge suppression, not absence; knowledge recovers when misleading framing is removed.
- Lightweight inference-time prompting substantially mitigates the bias without retraining; code is open-sourced.
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