arXiv:2607.05441v1 Announce Type: cross Abstract: Integrating external tools with Large Language Models (LLMs) has emerged as a promising paradigm for accomplishing complex tasks. Since LLMs still struggle to effectively manage large tool collections, researchers have begun exploring retrieval-based methods to pre-select the most relevant options, addressing input length and latency constraints. However, existing retrievers are often misaligned with tool-calling LLMs due to their separate training processes. This paper presents PORTS, a novel odds ratio preference optimization method for train
The proliferation of external tools for LLMs necessitates more sophisticated methods for tool selection to maintain efficiency and effectiveness as LLM capabilities expand.
Improving how LLMs interact with external tools directly enhances their practical utility and scalability for complex tasks, advancing the capabilities of autonomous AI agents.
This research introduces a preference-optimized retrieval method, PORTS, that better aligns tool selection with the LLM's intended use, moving beyond separately trained retrieval systems.
- · AI Agent Developers
- · Enterprises deploying LLM-powered solutions
- · Cloud AI providers
- · Companies with inefficient tool-calling LLM architectures
- · Legacy AI integration platforms
LLMs can efficiently manage larger and more diverse tool collections, significantly extending their practical application scope.
This efficiency could accelerate the development and deployment of more capable and reliable AI agents for complex, real-world tasks.
The enhanced performance of tool-integrated LLMs might lead to the automation of entire workflow segments previously requiring significant human oversight, impacting white-collar employment.
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Read at arXiv cs.AI