通过激活引导的认知行为建模：CBMAS
CBMAS: Cognitive Behavioral Modeling via Activation Steering
AI总结：
CBMAS通过连续激活引导技术，提升大型语言模型的认知行为可解释性，提供诊断框架和数据集以分析模型行为演变。
摘要：
大型语言模型（LLMs）在提示、层和上下文之间往往以不可预测的方式编码认知行为，这使得它们难以诊断和控制。我们提出了CBMAS，一种用于连续激活引导的诊断框架，该框架将认知偏差分析从离散的前后干预扩展到可解释的轨迹。通过结合引导向量构造与密集α扫掠、基于logit镜头的偏差曲线以及层位敏感性分析，我们的方法可以揭示临界点，即小干预强度会翻转模型行为，并展示引导效应在层深度上的演变。我们主张这些连续诊断在高层行为评估和低层表示动态之间架起桥梁，有助于LLMs的认知可解释性。最后，我们提供了CLI和各种认知行为的数据集，可在项目存储库中找到，https://github.com/shimamooo/CBMAS。
英文摘要：
Large language models (LLMs) often encode cognitive behaviors unpredictably across prompts, layers, and contexts, making them difficult to diagnose and control. We present CBMAS, a diagnostic framework for continuous activation steering, which extends cognitive bias analysis from discrete before/after interventions to interpretable trajectories. By combining steering vector construction with dense α-sweeps, logit lens-based bias curves, and layer-site sensitivity analysis, our approach can reveal tipping points where small intervention strengths flip model behavior and show how steering effects evolve across layer depth. We argue that these continuous diagnostics offer a bridge between high-level behavioral evaluation and low-level representational dynamics, contributing to the cognitive interpretability of LLMs. Lastly, we provide a CLI and datasets for various cognitive behaviors at the project repository, https://github.com/shimamooo/CBMAS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
