Middleware For Llms: Tools Are Instrumental For Language Agents In Complex Environments

Gu Yu, Shu Yiheng, Yu Hao, Liu Xiao, Dong Yuxiao, Tang Jie, Srinivasa Jayanth, Latapie Hugo, Su Yu. Arxiv 2024

[Paper]    
Agentic Applications Fine Tuning GPT Model Architecture Reinforcement Learning Tools

The applications of large language models (LLMs) have expanded well beyond the confines of text processing, signaling a new era where LLMs are envisioned as generalist language agents capable of operating within complex real-world environments. These environments are often highly expansive, making it impossible for the LLM to process them within its short-term memory. Motivated by recent research on extending the capabilities of LLMs with tools, this paper investigates the intriguing potential of tools to augment LLMs in handling such complexity. To this end, we design customized tools to aid in the proactive exploration within these massive environments. Such tools can serve as a middleware layer shielding the LLM from environmental complexity. In two representative complex environments – knowledge bases (KBs) and databases – we demonstrate the significant potential of augmenting language agents with tools in complex environments. Notably, equipped with these tools, GPT-4 achieves 2.8X the performance of the best baseline in tasks requiring access to database content and 2.2X in KB tasks. Our findings illuminate the path for advancing language agents in complex real-world applications.

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