Tag: Interpretability
All the articles with the tag "Interpretability".
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Racing Thoughts: Explaining Contextualization Errors in Large Language Models
Published: at 12:18 AM
74.82 🤔本文提出‘LLM Race Conditions Hypothesis’解释大型语言模型的上下文化错误，通过机械可解释性技术验证了关键窗口和上下文化顺序对模型性能的影响，并探索了推理时干预措施来缓解问题。
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HyPerAlign: Hypotheses-driven Personalized Alignment
Published: at 04:32 PM 65.01 🤔本文提出HyPerAlign方法，通过假设驱动的少样本学习实现LLM的个性化对齐，提高了模型对个体用户的适应性和安全性，同时减少了对微调的依赖。
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Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders
Published: at 10:58 AM 61.78 🤔This paper uses Sparse Autoencoders to identify and manipulate language-specific features in Large Language Models, introducing a monolinguality metric, demonstrating context dependency via code-switching, and enhancing steering vectors for better control over multilingual generation while revealing significant language-specific impacts through ablation studies.
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Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision
Published: at 04:27 PM 59.70 🤔本文提出Instruct-LF方法，通过结合LLMs的指令遵循能力和梯度-based统计模型，实现无需任务监督的目标导向潜在因素发现，提高了下游任务性能并在人工评估中被偏好。
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Empirical Evaluation of Progressive Coding for Sparse Autoencoders
Published: at 04:33 PM 51.36 🤔本文通过实证评估比较了Matryoshka SAEs和基于字典幂律修剪的方法，以实现SAEs的渐进式编码，提高计算效率、重建保真度和可解释性。