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 A.
In this section, we show examples of TOOLDEC preventing tool-related errors on various baselines. Baselines are displayed in the left column and TOOLDEC is showed on the right. Figure 7: TOOLDEC can prevent function name error, function argument error, and invalid ReAct syntax on ToolLLM. A.4 EXAMPLES OF KAMEL RELATIONS This paper is available on arxiv under CC 4.0 DEED license.
In this section, we show examples of TOOLDEC preventing tool-related errors on various baselines. Baselines are displayed in the left column and TOOLDEC is showed on the right. Figure 7: TOOLDEC can prevent function name error, function argument error, and invalid ReAct syntax on ToolLLM. A.4 EXAMPLES OF KAMEL RELATIONS This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license.
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