Summary of Methanol: Modularized Thinking Language Models with Intermediate Layer Thinking, Decoding and Bootstrapping Reasoning, by Ningyuan Xi et al.
MeTHanol: Modularized Thinking Language Models with Intermediate Layer Thinking, Decoding and Bootstrapping Reasoning
by Ningyuan Xi, Xiaoyu Wang, Yetao Wu, Teng Chen, Qingqing Gu, Yue Zhao, Jinxian Qu, Zhonglin Jiang, Yong Chen, Luo Ji
First submitted to arxiv on: 18 Sep 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 This research paper proposes a novel model architecture called TaS, which enhances the thinking ability of Large Language Models (LLMs). Unlike previous studies, this approach is data-driven and training-based. The authors draw inspiration from cognitive mechanisms in the natural world and design TaS to first consider thoughts before generating responses based on queries. The model features several pipelines for annotating or generating thought contents from prompt-response samples and a middle layer that behaves as a thinking layer. The language model is trained using thought-augmented data, allowing it to automatically generate reasonable thoughts and output more rational responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a better way for computers to understand and respond to human expressions by making them think like humans do. The researchers designed a new kind of language model called TaS that considers what someone is thinking before responding. This is different from other approaches, which just focus on generating text without really thinking about the meaning. The team trained TaS using special data that includes thoughts and showed that it can generate more reasonable responses. |
Keywords
» Artificial intelligence » Language model » Prompt
