Interpretable Next-token Prediction via the Generalized Induction Head
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arXiv
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| Format: | Preprint |
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2024
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| author | Kim, Eunji Mantena, Sriya Yang, Weiwei Singh, Chandan Yoon, Sungroh Gao, Jianfeng |
| author_facet | Kim, Eunji Mantena, Sriya Yang, Weiwei Singh, Chandan Yoon, Sungroh Gao, Jianfeng |
| contents | While large transformer models excel in predictive performance, their lack of interpretability restricts their usefulness in high-stakes domains. To remedy this, we propose the Generalized Induction-Head Model (GIM), an interpretable model for next-token prediction inspired by the observation of "induction heads" in LLMs. GIM is a retrieval-based module that identifies similar sequences in the input context by combining exact n-gram matching and fuzzy matching based on a neural similarity metric. We evaluate GIM in two settings: language modeling and fMRI response prediction. In language modeling, GIM improves next-token prediction by up to 25%p over interpretable baselines, significantly narrowing the gap with black-box LLMs. In an fMRI setting, GIM improves neural response prediction by 20% and offers insights into the language selectivity of the brain. GIM represents a significant step toward uniting interpretability and performance across domains. The code is available at https://github.com/ejkim47/generalized-induction-head. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00066 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interpretable Next-token Prediction via the Generalized Induction Head Kim, Eunji Mantena, Sriya Yang, Weiwei Singh, Chandan Yoon, Sungroh Gao, Jianfeng Computation and Language Artificial Intelligence Machine Learning While large transformer models excel in predictive performance, their lack of interpretability restricts their usefulness in high-stakes domains. To remedy this, we propose the Generalized Induction-Head Model (GIM), an interpretable model for next-token prediction inspired by the observation of "induction heads" in LLMs. GIM is a retrieval-based module that identifies similar sequences in the input context by combining exact n-gram matching and fuzzy matching based on a neural similarity metric. We evaluate GIM in two settings: language modeling and fMRI response prediction. In language modeling, GIM improves next-token prediction by up to 25%p over interpretable baselines, significantly narrowing the gap with black-box LLMs. In an fMRI setting, GIM improves neural response prediction by 20% and offers insights into the language selectivity of the brain. GIM represents a significant step toward uniting interpretability and performance across domains. The code is available at https://github.com/ejkim47/generalized-induction-head. |
| title | Interpretable Next-token Prediction via the Generalized Induction Head |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2411.00066 |