86 citations · 109 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method
Chen Yang, Sunhao Dai, Yupeng Hou +4
Reciprocal recommender systems~(RRS), conducting bilateral recommendations between two involved parties, have gained increasing attention for enhancing matching efficiency. However…
ReCODE: Modeling Repeat Consumption with Neural ODE
Sunhao Dai, Changle Qu, Sirui Chen +2
In real-world recommender systems, such as in the music domain, repeat consumption is a common phenomenon where users frequently listen to a small set of preferred songs or artists…
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…