86 citations · 109 across the 11 of their papers we have counts for
4 papers · 2 filters
Length-Induced Embedding Collapse in PLM-based Models
Yuqi Zhou, Sunhao Dai, Zhanshuo Cao +2
Text embeddings from PLM-based models enable a wide range of applications, yet their performance often degrades on longer texts. In this paper, we introduce a phenomenon we call Le…
From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions
Changle Qu, Sunhao Dai, Xiaochi Wei +5
Tool learning enables Large Language Models (LLMs) to interact with external environments by invoking tools, serving as an effective strategy to mitigate the limitations inherent i…
Tool Learning with Large Language Models: A Survey
Changle Qu, Sunhao Dai, Xiaochi Wei +5
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. Despite gro…
Towards Completeness-Oriented Tool Retrieval for Large Language Models
Changle Qu, Sunhao Dai, Xiaochi Wei +5
Recently, integrating external tools with Large Language Models (LLMs) has gained significant attention as an effective strategy to mitigate the limitations inherent in their pre-t…