5 citations · 36 across the 28 of their papers we have counts for
17 papers · 1 filter
RecThinker: An Agentic Framework for Tool-Augmented Reasoning in Recommendation
Haobo Zhang, Yutao Zhu, Kelong Mao +2
Large Language Models (LLMs) have revolutionized recommendation agents by providing superior reasoning and flexible decision-making capabilities. However, existing methods mainly f…
ChatShopBuddy: Towards Reliable Conversational Shopping Agents via Reinforcement Learning
Yiruo Cheng, Kelong Mao, Tianhao Li +3
Conversational shopping agents represent a critical consumer-facing application of Large Language Model (LLM)-powered agents, yet how to effectively apply post-training Reinforceme…
CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation
Yiruo Cheng, Kelong Mao, Ziliang Zhao +6
Retrieval-Augmented Generation (RAG) has become a powerful paradigm for enhancing large language models (LLMs) through external knowledge retrieval. Despite its widespread attentio…
Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search
Fengran Mo, Chen Qu, Kelong Mao +4
Conversational search supports multi-turn user-system interactions to solve complex information needs. Different from the traditional single-turn ad-hoc search, conversational sear…
Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation
Chenlong Deng, Kelong Mao, Zhicheng Dou
Legal case retrieval for sourcing similar cases is critical in upholding judicial fairness. Different from general web search, legal case retrieval involves processing lengthy, com…
CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search
Fengran Mo, Abbas Ghaddar, Kelong Mao +4
In this paper, we study how open-source large language models (LLMs) can be effectively deployed for improving query rewriting in conversational search, especially for ambiguous qu…