LogitLens4LLMs: Extending Logit Lens Analysis to Modern Large Language Models

Abstract: This paper introduces LogitLens4LLMs, a toolkit that extends the Logit Lens technique to modern LLMs. While Logit Lens has been a crucial method for understanding internal representations of LLMs, it was previously limited to earlier model architectures. Our work overcomes the limitations of existing implementations, enabling the technique to be applied to state-of-the-art architectures (such as Qwen-2.5 and Llama-3.1) while automating key analytical workflows. By developing component-specific hooks to capture both attention mechanisms and MLP outputs, our implementation achieves full compatibility with the HuggingFace transformer library while maintaining low inference overhead. The toolkit provides both interactive exploration and batch processing capabilities, supporting large-scale layer-wise analyses. Through open-sourcing our implementation, we aim to facilitate deeper investigations into the internal mechanisms of large-scale LLMs. The toolkit is openly available at https://github.com/zhenyu-02/LogitLens4LLMs.