Recently, tool learning with large language models (LLMs) has emerged as a promising
paradigm for augmenting the capabilities of LLMs to tackle highly complex problems …

Tool learning empowers large language models (LLMs) as agents to use external tools and
extend their utility. Existing methods employ one single LLM-based agent to iteratively select …

Recent large language models (LLMs) advancements sparked a growing research interest
in tool assisted LLMs solving real-world challenges, which calls for comprehensive …

In various industrial fields of human social development, people have been exploring
methods aimed at freeing human labor. Constructing LLM-based agents is considered to be …

In this work, we introduce Speech-Copilot, a modular framework for instruction-oriented
speech-processing tasks that minimizes human effort in toolset construction. Unlike end-to …

Tool learning enables large language models (LLMs) to interact with external tools and
APIs, greatly expanding the application scope of LLMs. However, due to the dynamic nature …

Large Language Models (LLMs) have exhibited significant potential in performing diverse
tasks, including the ability to call functions or use external tools to enhance their …

Tool-augmented large language models (LLMs) are rapidly being integrated into real-world
applications. Due to the lack of benchmarks, the community has yet to fully understand the …

As large language models (LLMs) advance, their inability to autonomously execute tasks by
directly interacting with external tools remains a critical limitation. Traditional methods rely …

Multi-step reasoning ability of large language models is crucial in tasks such as math and
tool utilization. Current researches predominantly focus on enhancing model performance in …