Interpretable Language Modeling via Induction-head Ngram Models

• URL: http://arxiv.org/abs/2411.00066v1
• Date: Thu, 31 Oct 2024 12:33:26 GMT
• Title: Interpretable Language Modeling via Induction-head Ngram Models
• Authors: Eunji Kim, Sriya Mantena, Weiwei Yang, Chandan Singh, Sungroh Yoon, Jianfeng Gao,
• Abstract summary: We propose Induction-head ngram models (Induction-Gram) to bolster modern ngram models with a hand-engineered "induction head" This induction head uses a custom neural similarity metric to efficiently search the model's input context for potential next-word completions. Experiments show that this simple method significantly improves next-word prediction over baseline interpretable models.
• Score: 74.26720927767398
• License:
• Abstract: Recent large language models (LLMs) have excelled across a wide range of tasks, but their use in high-stakes and compute-limited settings has intensified the demand for interpretability and efficiency. We address this need by proposing Induction-head ngram models (Induction-Gram), a method that builds an efficient, interpretable LM by bolstering modern ngram models with a hand-engineered "induction head". This induction head uses a custom neural similarity metric to efficiently search the model's input context for potential next-word completions. This process enables Induction-Gram to provide ngram-level grounding for each generated token. Moreover, experiments show that this simple method significantly improves next-word prediction over baseline interpretable models (up to 26%p) and can be used to speed up LLM inference for large models through speculative decoding. We further study Induction-Gram in a natural-language neuroscience setting, where the goal is to predict the next fMRI response in a sequence. It again provides a significant improvement over interpretable models (20% relative increase in the correlation of predicted fMRI responses), potentially enabling deeper scientific investigation of language selectivity in the brain. The code is available at https://github.com/ejkim47/induction-gram.

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