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About this paper
Simranjit Singh, Andreas Karatzas, Michael Fore, Iraklis Anagnostopoulos, Dimitrios Stamoulis
This paper presents an LLM-Tool Compiler that enhances the efficiency of LLM function calling by dynamically fusing similar tool operations before they are presented to the language model. The system reduces token costs by up to 1.4x and latency by up to 39%, while increasing parallel tool calls by up to five times on the GeoLLM-Engine benchmark.
Key insights
- Dynamic identification and fusion of logically similar tool operations into unified tasks before presentation to the LLM can significantly reduce API round-trips.
- Applying hardware compiler principles, such as operation fusion (similar to multiply-add), to LLM tool interaction can enhance parallelization and system efficiency.
- A modular, agent-agnostic compiler approach allows for seamless integration with existing LLM reasoning schemes (e.g., CoT, ReAct) with minimal engineering overhead.
Summary by alphaXiv.
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