Summary of Beats: Optimizing Llm Mathematical Capabilities with Backverify and Adaptive Disambiguate Based Efficient Tree Search, by Linzhuang Sun et al.
BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search
by Linzhuang Sun, Hao Liang, Jingxuan Wei, Bihui Yu, Conghui He, Zenan Zhou, Wentao Zhang
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 novel approach, BEATS, enhances the mathematical problem-solving abilities of Large Language Models (LLMs) by leveraging newly designed prompts that guide the model to iteratively rewrite and generate answers. This method improves upon previous techniques such as supervised fine-tuning (SFT), prompt engineering, and search-based methods by introducing a new back-verification technique that uses LLMs to validate the correctness of generated answers. BEATS also employs a pruning tree search to optimize search time while achieving strong performance on the MATH benchmark, outperforming GPT4’s 42.5 with an improved score of 61.52. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are very good at doing lots of things, like answering questions and generating text. But they’re not great at solving math problems. That’s because math is a specific type of problem that requires following rules and being logical. Some people have tried to make LLMs better at math by changing how they give the model instructions or by letting it search for answers in a special way. However, these efforts haven’t been very successful yet. To fix this, some researchers came up with a new idea called BEATS. It’s a method that helps LLMs solve math problems better by giving them step-by-step instructions and then checking the answer to make sure it’s correct. This approach improved the score on a math test from 36.94 to 61.52, beating other models like GPT4. |
Keywords
» Artificial intelligence » Fine tuning » Prompt » Pruning » Supervised
