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.
-enhanced versions on a variety of tasks involving tools like math functions, knowledge graph relations, and complex real-world RESTful APIs. Our experiments show that , a decoding algorithm guided by a finite-state machine to ensure LLMs invoke tools properly. Our core insight is to explicitly represent states during LLM decoding. Each state is associated with a valid set of tokens corresponding to tool names and tool arguments.
is able to always generate syntactically correct tool calls. Figure 1 illustrates that an LLM enhanced by automatically constructs a finite-state machine from a tool’s API signature and adds it to the existing FSM. , a finite-state decoding algorithm to empower LLMs to use tools properly. is more than 8x better than baselines on mathematical reasoning with 9 unseen tools and 7x better than knowledge question answering with 204 unseen tools. This paper is available on arxiv under CC 4.0 DEED license. We release our code and data at https://github.com/chenhongqiao/tooldec. 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.
-enhanced versions on a variety of tasks involving tools like math functions, knowledge graph relations, and complex real-world RESTful APIs. Our experiments show that , a decoding algorithm guided by a finite-state machine to ensure LLMs invoke tools properly. Our core insight is to explicitly represent states during LLM decoding. Each state is associated with a valid set of tokens corresponding to tool names and tool arguments.
is able to always generate syntactically correct tool calls. Figure 1 illustrates that an LLM enhanced by automatically constructs a finite-state machine from a tool’s API signature and adds it to the existing FSM.
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