Meta-Reasoning Improves Tool Use in Large Language Models

• URL: http://arxiv.org/abs/2411.04535v1
• Date: Thu, 07 Nov 2024 08:48:33 GMT
• Title: Meta-Reasoning Improves Tool Use in Large Language Models
• Authors: Lisa Alazraki, Marek Rei,
• Abstract summary: External tools help large language models (LLMs) succeed at tasks where they would otherwise typically fail. We present Tool selECTion via meta-reasONing (TECTON), a two-phase system that first reasons over a task using a custom fine-tuned LM head and outputs candidate tools.
• Score: 10.193264105560864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: External tools help large language models (LLMs) succeed at tasks where they would otherwise typically fail. In existing frameworks, LLMs learn tool use either by in-context demonstrations or via full model fine-tuning on annotated data. As these approaches do not easily scale, a recent trend is to abandon them in favor of lightweight, parameter-efficient tuning paradigms. These methods allow quickly alternating between the frozen LLM and its specialised fine-tuned version, by switching on or off a handful of additional custom parameters. Hence, we postulate that the generalization ability of the frozen model can be leveraged to improve tool selection. We present Tool selECTion via meta-reasONing (TECTON), a two-phase system that first reasons over a task using a custom fine-tuned LM head and outputs candidate tools. Then, with the custom head disabled, it meta-reasons (i.e., it reasons over the previous reasoning process) to make a final choice. We show that TECTON results in substantial gains - both in-distribution and out-of-distribution - on a range of math reasoning datasets.

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