6 citations · 7 across the 10 of their papers we have counts for
4 papers · 1 filter
QUIDS: Query Intent Description for Exploratory Search via Dual Space Modeling
Yumeng Wang, Xiuying Chen, Suzan Verberne
In exploratory search, users often submit vague queries to investigate unfamiliar topics, but receive limited feedback about how the search engine understood their input. This lead…
What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks
Chengrui Huang, Zhengliang Shi, Yuntao Wen +4
Tool learning methods have enhanced the ability of large language models (LLMs) to interact with real-world applications. Many existing works fine-tune LLMs or design prompts to en…
Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents
Zhengliang Shi, Shen Gao, Lingyong Yan +6
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, enabling them to solve practical tasks. Previous methods ma…
Learning to Use Tools via Cooperative and Interactive Agents
Zhengliang Shi, Shen Gao, Xiuyi Chen +7
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 sele…