Summary of Adapting While Learning: Grounding Llms For Scientific Problems with Intelligent Tool Usage Adaptation, by Bohan Lyu et al.
Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation
by Bohan Lyu, Yadi Cao, Duncan Watson-Parris, Leon Bergen, Taylor Berg-Kirkpatrick, Rose Yu
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Large Language Models (LLMs) have shown promise in solving simple scientific problems but struggle with complex ones, often producing unreliable results. To address this issue, a novel two-component fine-tuning method called Adapting While Learning (AWL) is proposed. The first component, World Knowledge Learning (WKL), involves LLMs learning from tool-generated solutions to internalize scientific knowledge. The second component, Tool Usage Adaptation (TUA), classifies questions as easy or hard based on the WKL-trained model’s accuracy and trains it to maintain direct reasoning for simple problems while switching to tools for challenging ones. This approach is validated on six scientific benchmark datasets in climate science, epidemiology, and mathematics, demonstrating a 28.27% increase in answer accuracy and 13.76% improvement in tool usage accuracy compared to the base 8B model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can solve simple problems but get stuck on complex ones. To make them better, scientists created a new way of teaching these models called Adapting While Learning (AWL). This method has two parts: one helps the model learn from scientific tools and the other makes it use those tools only when really needed. The scientists tested this approach with six sets of problems and found that it made the models much better at solving them. |
Keywords
* Artificial intelligence * Fine tuning
