Summary of Interpretable Language Modeling Via Induction-head Ngram Models, by Eunji Kim et al.
Interpretable Language Modeling via Induction-head Ngram Models
by Eunji Kim, Sriya Mantena, Weiwei Yang, Chandan Singh, Sungroh Yoon, Jianfeng Gao
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Induction-head ngram models (Induction-Gram) aim to address the demand for interpretability and efficiency in large language models (LLMs). This method builds upon modern ngram models by adding a hand-engineered “induction head” that uses a custom neural similarity metric to search for potential next-word completions. Induction-Gram provides ngram-level grounding for each generated token, leading to improved next-word prediction and faster LLM inference. The method is demonstrated in both general language tasks and a natural-language neuroscience setting, where it shows significant improvements over baseline interpretable models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Induction-Gram is a new way to make large language models better at understanding what they’re saying. It takes an existing model and adds a special part that helps it figure out the next word in a sentence. This makes the model more accurate and faster, which is helpful when you don’t have a lot of computer power. The researchers tested Induction-Gram with two different types of tasks: general language and something specific to neuroscience. In both cases, it did much better than other models that are designed to be easier to understand. |
Keywords
» Artificial intelligence » Grounding » Inference » Token
