48 citations · 50 across the 9 of their papers we have counts for
9 papers
SelfDR: Self-Distillation from Reasoning for LLM-Based Recommendation
Chumeng Jiang, Jiayin Wang, Xinjie Lin +3
Large Language Models (LLMs) have recently emerged as powerful backbones for recommendation. To better elicit their capabilities, reasoning has been widely incorporated to help LLM…
Loong: A Human-Like Long Document Translation Agent with Observe-and-Act Adaptive Context Selection
Yutong Wang, Xuebo Liu, Derek F. Wong +5
Document-level translation remains one of the most challenging tasks for large language models, which are constrained by limited context windows that impede global cohesion, while…
User-Aware Active Knowledge Acquisition for Emotional Support Dialogue
Mufan Xu, Kehai Chen, Jiahao Hu +4
Emotional support plays an important role in dialogue systems, and its success depends on adapting to a user's evolving and implicit needs across multi-turn interactions while leve…
Personalized Turn-Level User Conversation Satisfaction Benchmark
Zhefan Wang, Zhiqiang Guo, Weizhi Ma +3
User satisfaction with AI assistants is highly personalized: the same response may satisfy one user but disappoint another depending on what each user expects and what they have as…
AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
Yu Shang, Peijie Liu, Yuwei Yan +9
The emergence of agentic recommender systems powered by Large Language Models (LLMs) represents a paradigm shift in personalized recommendations, leveraging LLMs' advanced reasonin…
Beyond Utility: Evaluating LLM as Recommender
Chumeng Jiang, Jiayin Wang, Weizhi Ma +4
With the rapid development of Large Language Models (LLMs), recent studies employed LLMs as recommenders to provide personalized information services for distinct users. Despite ef…
StepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning
Yuanqing Yu, Zhefan Wang, Weizhi Ma +4
Despite their powerful text generation capabilities, large language models (LLMs) still struggle to effectively utilize external tools to solve complex tasks, a challenge known as…
MACRec: a Multi-Agent Collaboration Framework for Recommendation
Zhefan Wang, Yuanqing Yu, Wendi Zheng +2
LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks. Recognizing the current gap in leveraging agent capabiliti…
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems
Yuanqing Yu, Chongming Gao, Jiawei Chen +5
Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising attention for their potential to enhance long-term user engagement. However, research in this field f…