Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation

TL;DR

This paper introduces a novel two-component fine-tuning method called Adapting While Learning (AWL) that improves scientific problem-solving by LLMs through internalizing knowledge and adaptive tool usage, reducing hallucinations and costs.

Contribution

The paper proposes a new fine-tuning approach that enables LLMs to internalize scientific knowledge and adapt tool usage based on problem difficulty, outperforming existing models.

Findings

29.11% higher answer accuracy with AWL

12.72% better tool usage accuracy

Surpasses GPT-4o and Claude-3.5 on multiple datasets

Abstract

Large Language Models (LLMs) demonstrate promising capabilities in solving scientific problems but often suffer from the issue of hallucination. While integrating LLMs with tools can mitigate this issue, models fine-tuned on tool usage become overreliant on them and incur unnecessary costs. Inspired by how human experts assess problem complexity before selecting solutions, we propose a novel two-component fine-tuning method, Adapting While Learning (AWL). In the first component, World Knowledge Learning (WKL), LLMs internalize scientific knowledge by learning from tool-generated solutions. In the second component, Tool Usage Adaptation (TUA), we categorize problems as easy or hard based on the model's accuracy, and train it to maintain direct reasoning for easy problems while switching to tools for hard ones. We validate our method on six scientific benchmark datasets across climate science, epidemiology, physics, and other domains. Compared to the original instruct model (8B), models post-trained with AWL achieve 29.11% higher answer accuracy and 12.72% better tool usage accuracy, even surpassing state-of-the-art models including GPT-4o and Claude-3.5 on four custom-created datasets. Our code is open-source at https://github.com/Rose-STL-Lab/Adapting-While-Learning.