Researchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use.
Authors: Kexun Zhang, UC Santa Barbara and Equal contribution; Hongqiao Chen, Northwood High School and Equal contribution; Lei Li, Carnegie Mellon University; William Yang Wang,UC Santa Barbara. Table of Links Abstract and Intro Related Work ToolDec: LLM Tool Use via Finite-State Decoding Experiment: ToolDec Eliminates Syntax Errors Experiment: ToolDec Enables Generalizable Tool Selection Conclusion and References Appendix 2.
Fine-tuning language models to use tools. Language models can be fine-tuned to use tools with data that contain interleaving text and tool use. Earlier studies make language models use a single tool like a retrieval module or a search engine by fine-tuning. Recent advances in tool-augmented language models that use multiple tools also fine-tune language models to use tools including QA models, translation models, calculators, and search engines.
Fine-tuning language models to use tools. Language models can be fine-tuned to use tools with data that contain interleaving text and tool use. Earlier studies make language models use a single tool like a retrieval module or a search engine by fine-tuning. Recent advances in tool-augmented language models that use multiple tools also fine-tune language models to use tools including QA models, translation models, calculators, and search engines.
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