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Syntax Error-Free and Generalizable Tool Use for LLMs: Appendix
by Writings, Papers and Blogs on Text ModelsJune 2nd, 2024
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Researchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use.
Authors:
(1) Kexun Zhang, UC Santa Barbara and Equal contribution;
(2) Hongqiao Chen, Northwood High School and Equal contribution;
(3) Lei Li, Carnegie Mellon University;
(4) 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. APPENDIX
A.1 PSEUDO-CODE OF THE DECODING ALGORITHM
A.2 GENERALIZING TOOLKENGPT TO UNSEEN NEW TOOLS
A.3 EXAMPLES OF TOOLDEC ELIMINATING TOOL-RELATED ERRORS
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.
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