Table 1 From Executable Code Actions Elicit Better Llm Agents To this end, we collect an instruction tuning dataset codeactinstruct that consists of 7k multi turn interactions using codeact. we show that it can be used with existing data to improve models in agent oriented tasks without compromising their general capability. This work presents codenav, an llm agent that navigates and leverages previously unseen code repositories to solve user queries, and quantitatively compares the effectiveness of code use to tool use and the effect of varying kinds of tool and library descriptions on code use performance.
Executable Code Actions Elicit Better Llm Agents Csdn博客 Our extensive analysis of 17 llms on api bank and a newly curated benchmark shows that codeact outperforms widely used alternatives (up to 20% higher success rate). On average, the paper shows that code actions require 30% fewer steps than json, which amounts to an equivalent reduction in the tokens generated. since llm calls are often the dimensioning cost of agent systems, it means your agent system runs are ~30% cheaper. Integrated with a python interpreter, codeact can execute code actions and dynamically revise prior actions or emit new actions upon new observations (e.g., code execution results) through multi turn interactions (check out this example!). Our extensive analysis of 17 llms on api bank and a newly curated benchmark shows that codeact outperforms widely used alternatives (up to 20% higher success rate).
Executable Code Actions Elicit Better Llm Agents A Kaboyo Collection Integrated with a python interpreter, codeact can execute code actions and dynamically revise prior actions or emit new actions upon new observations (e.g., code execution results) through multi turn interactions (check out this example!). Our extensive analysis of 17 llms on api bank and a newly curated benchmark shows that codeact outperforms widely used alternatives (up to 20% higher success rate). Published in icml, 2024. recommended citation: xingyao wang, yangyi chen, lifan yuan, yizhe zhang, yunzhu li, hao peng, heng ji arxiv.org abs 2402.01030. Taskweaver is proposed as a code first framework for building llm powered autonomous agents that converts user requests into executable code and treats user defined plugins as callable functions. This document introduces codeact, a framework that enhances large language model (llm) agents by enabling them to execute python code, thus expanding their action space for complex real world tasks.
Paper Page Executable Code Actions Elicit Better Llm Agents Published in icml, 2024. recommended citation: xingyao wang, yangyi chen, lifan yuan, yizhe zhang, yunzhu li, hao peng, heng ji arxiv.org abs 2402.01030. Taskweaver is proposed as a code first framework for building llm powered autonomous agents that converts user requests into executable code and treats user defined plugins as callable functions. This document introduces codeact, a framework that enhances large language model (llm) agents by enabling them to execute python code, thus expanding their action space for complex real world tasks.
Figure 1 From Executable Code Actions Elicit Better Llm Agents This document introduces codeact, a framework that enhances large language model (llm) agents by enabling them to execute python code, thus expanding their action space for complex real world tasks.
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