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Transformer-based Large Language Models (LLMs) are the state-of-the-art fornatural language tasks. Recent work has attempted to decode, by reverseengineering the role of linear layers, the internal mechanisms by which LLMsarrive at their final predictions for text completion tasks. Yet little isknown about the specific role of attention heads in producing the final tokenprediction. We propose Attention Lens, a tool that enables researchers totranslate the outputs of attention heads into vocabulary tokens via learnedattention-head-specific transformations called lenses. Preliminary findingsfrom our trained lenses indicate that attention heads play highly specializedroles in language models. The code for Attention Lens is available atgithub.com/msakarvadia/AttentionLens.

Attention Lens: A Tool for Mechanistically Interpreting the Attention Head Information Retrieval Mechanism