Luick, N. (2024). Universal Response and Emergence of Induction in LLMs (Preprint). arXiv:2411.07071v1 [cs.LG].
This research paper investigates the emergence of induction behavior in large language models (LLMs) and aims to understand how this behavior is composed within the model's architecture.
The authors introduce a novel method of probing the response of LLMs to weak, single-token perturbations within the residual stream. By analyzing the model's response to these perturbations, they can identify and quantify correlations between tokens, revealing signatures of induction behavior. The study focuses on three LLMs: Gemma-2-2B, Llama-3.2-3B, and GPT-2-XL.
The study demonstrates that induction behavior, a crucial mechanism for in-context learning, emerges from the collective interplay of components within LLMs. The proposed method of residual stream perturbation analysis provides valuable insights into this complex behavior and serves as a benchmark for large-scale circuit analysis in LLMs.
This research significantly contributes to the field of mechanistic interpretability by providing a novel method for analyzing the emergence of complex behaviors like induction in LLMs. The findings enhance our understanding of how LLMs learn and process information, paving the way for building more interpretable and reliable AI systems.
The study primarily focuses on repeated sequences of random tokens and does not explore the applicability of the method to real text sequences. Future research could investigate the impact of higher-order correlations between tokens and the universality of scale-invariance across a wider range of LLM architectures and sizes.
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by Niclas Luick at arxiv.org 11-12-2024
https://arxiv.org/pdf/2411.07071.pdfDeeper Inquiries