The content delves into the study of linear representations in large language models, focusing on the theoretical framework and experimental validation. It discusses the role of log-odds matching and gradient descent in promoting linearity, along with experiments to confirm these findings.
Recent works have highlighted that high-level semantic concepts are encoded linearly in large language models. The study aims to understand this phenomenon by introducing a latent variable model for next token prediction. The model abstracts concept dynamics and formalizes underlying human-interpretable concepts.
Experiments show that linear representations emerge when learning from data matching the latent variable model. The results support the theory that both log-odds matching and gradient descent contribute to promoting linear structures in large language models.
The content also touches upon orthogonal representations observed in LLMs, discussing how Euclidean geometry can encode semantics despite not being identified by standard LLM objectives. Experiments validate theoretical insights on alignment between embedding and unembedding vectors.
Overall, the content provides a comprehensive analysis of the origins of linear representations in large language models, offering insights into theoretical frameworks, experimental validations, and implications for understanding semantic encoding.
다른 언어로
소스 콘텐츠 기반
arxiv.org
더 깊은 질문