10 papers
Do Generative Recommenders Deepen the Information Cocoon? A Closed-Loop Simulation with LLM-powered User Simulators
Jiyuan Yang, Gengxin Sun, Mengqi Zhang +5
Recommender systems alleviate information overload, yet repeated feedback between recommendations and user interactions can reinforce existing preferences and narrow users' exposur…
AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent
Yinyi Luo, Yiqiao Jin, Weichen Yu +6
While large language model (LLM) multi-agent systems achieve superior reasoning performance through iterative debate, practical deployment is limited by their high computational co…
SayNext-Bench: Why Do LLMs Struggle with Next-Utterance Anticipation?
Yueyi Yang, Haotian Liu, Fang Kang +4
We explore the use of large language models (LLMs) for next-utterance anticipation in human dialogue. Despite recent advances in LLMs demonstrating their ability to engage in natur…
Disentangling Knowledge Representations for Large Language Model Editing
Mengqi Zhang, Zisheng Zhou, Xiaotian Ye +4
Knowledge Editing has emerged as a promising solution for efficiently updating embedded knowledge in large language models (LLMs). While existing approaches demonstrate effectivene…
Mixture-of-RAG: Integrating Text and Tables with Large Language Models
Chi Zhang, Qiyang Chen, Mengqi Zhang
Large language models (LLMs) achieve optimal utility when their responses are grounded in external knowledge sources. However, real-world documents, such as annual reports, scienti…
Next Edit Prediction: Learning to Predict Code Edits from Context and Interaction History
Ruofan Lu, Yintong Huo, Meng Zhang +2
The rapid advancement of large language models (LLMs) has led to the widespread adoption of AI-powered coding assistants integrated into a development environment. On one hand, low…