仅用模型隐状态就能估算问题难度，省去重复生成。
The LLM Already Knows: Estimating LLM-Perceived Question Difficulty via Hidden Representations
- 用隐藏表示建模生成过程为马尔可夫链，估质量期望。
- 在文本与多模态任务中均优于现有方法，准确率更高。
- 可用于自适应推理，减少生成次数提升效率。
估算大语言模型（LLM）感知的问题难度对性能评估和自适应推理至关重要。现有方法通常依赖重复采样、辅助模型或微调目标模型，可能带来高计算开销或降低通用性。本文提出一种新方法，仅利用目标LLM产生的隐藏表示进行难度估计。我们将分词级生成过程建模为马尔可夫链，并定义价值函数，基于任意隐藏状态估计预期输出质量。该方法仅需初始隐藏状态即可实现高效精准的难度估计，无需生成任何输出标记。在文本与多模态任务上的大量实验表明，本方法始终优于现有基线。进一步将难度估计用于引导自洽、Best-of-N和Self-Refine等自适应推理策略，在生成更少标记的前提下实现了更高的推理效率。
原文摘要 · Abstract (English)
Estimating the difficulty of input questions as perceived by large language models (LLMs) is essential for accurate performance evaluation and adaptive inference. Existing methods typically rely on repeated response sampling, auxiliary models, or fine-tuning the target model itself, which may incur substantial computational costs or compromise generality. In this paper, we propose a novel approach for difficulty estimation that leverages only the hidden representations produced by the target LLM. We model the token-level generation process as a Markov chain and define a value function to estimate the expected output quality given any hidden state. This allows for efficient and accurate difficulty estimation based solely on the initial hidden state, without generating any output tokens. Extensive experiments across both textual and multimodal tasks demonstrate that our method consistently outperforms existing baselines in difficulty estimation. Moreover, we apply our difficulty estimates to guide adaptive reasoning strategies, including Self-Consistency, Best-of-N, and Self-Refine, achieving higher inference efficiency with fewer generated tokens.
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