大模型说一套做一套，看似理性实则伪思辨。
Pseudo-Deliberation in Language Models: When Reasoning Fails to Align Values and Actions
- 设计框架VALDI，量化模型言行一致性
- 4941个场景测试，发现多数模型言行不一
- 提出多智能体审计器干预生成过程
大型语言模型常以表达的价值观被评估，但这些价值观并不总是转化为实际行为，这种现象称为“价值-行动差距”。本文认为，即使在明确推理的情况下，这一差距依然存在，揭示了一种更深层的失效模式——“伪思辨”：表面上有条理的推理，却没有对应的行为一致。为系统研究该问题，我们提出了VALDI框架，用于测量陈述价值与生成对话间的对齐程度。VALDI包含跨五个领域的4,941个人类中心场景，三个任务（诱发价值表述、推理和行为），以及五种量化价值遵循度的指标。在多个专有和开源语言模型中，我们均观察到表达的价值与后续对话之间存在持续的不一致。为进一步探索干预策略，我们提出VIVALDI，一种在生成不同阶段介入的多智能体价值审计系统。
原文摘要 · Abstract (English)
Large language models (LLMs) are often evaluated based on their stated values, yet these do not reliably translate into their actions, a discrepancy termed "value-action gap." In this work, we argue that this gap persists even under explicit reasoning, revealing a deeper failure mode we call "Pseudo-Deliberation": the appearance of principled reasoning without corresponding behavioral alignment. To study this systematically, we introduce VALDI, a framework for measuring alignment between stated values and generated dialogue. VALDI includes 4,941 human-centered scenarios across five domains, three tasks that elicit value articulation, reasoning, and action, and five metrics for quantifying value adherence. Across both proprietary and open-source LLMs, we observe consistent misalignment between expressed values and downstream dialogues. To investigate intervention strategies, we propose VIVALDI, a multi-agent value auditor that intervenes at different stages of generation.
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