1.5k citations · 1.8k across the 16 of their papers we have counts for
16 papers
RecNet: Self-Evolving Preference Propagation for Agentic Recommender Systems
Bingqian Li, Xiaolei Wang, Junyi Li +5
Agentic recommender systems leverage Large Language Models (LLMs) to model complex user behaviors and support personalized decision-making. However, existing methods primarily mode…
LARES: Latent Reasoning for Sequential Recommendation
Enze Liu, Bowen Zheng, Xiaolei Wang +4
Sequential recommender systems have become increasingly important in real-world applications that model user behavior sequences to predict their preferences. However, existing sequ…
DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation
Bowen Zheng, Xiaolei Wang, Enze Liu +5
Recently, large language models (LLMs) have been introduced into recommender systems (RSs), either to enhance traditional recommendation models (TRMs) or serve as recommendation ba…
Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User
Xiaolei Wang, Chunxuan Xia, Junyi Li +5
Conversational recommendation systems (CRSs) use multi-turn interaction to capture user preferences and provide personalized recommendations. A fundamental challenge in CRSs lies i…
Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering
Xinyu Tang, Xiaolei Wang, Zhihao Lv +5
Recent advancements in long chain-of-thoughts(long CoTs) have significantly improved the reasoning capabilities of large language models(LLMs). Existing work finds that the capabil…
DAWN-ICL: Strategic Planning of Problem-solving Trajectories for Zero-Shot In-Context Learning
Xinyu Tang, Xiaolei Wang, Wayne Xin Zhao +1
Zero-shot in-context learning (ZS-ICL) aims to conduct in-context learning (ICL) without using human-annotated demonstrations. Most ZS-ICL methods use large language models (LLMs)…
YuLan: An Open-source Large Language Model
Yutao Zhu, Kun Zhou, Kelong Mao +35
Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many o…
Investigating the Pre-Training Dynamics of In-Context Learning: Task Recognition vs. Task Learning
Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao +1
The emergence of in-context learning (ICL) is potentially attributed to two major abilities: task recognition (TR) for recognizing the task from demonstrations and utilizing pre-tr…
Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers
Xinyu Tang, Xiaolei Wang, Wayne Xin Zhao +3
Automatic prompt optimization is an important approach to improving the performance of large language models (LLMs). Recent research demonstrates the potential of using LLMs as pro…
Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models
Tianyi Tang, Wenyang Luo, Haoyang Huang +5
Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora. It remains a challenging…
Alleviating the Long-Tail Problem in Conversational Recommender Systems
Zhipeng Zhao, Kun Zhou, Xiaolei Wang +4
Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations. To develop an effective CRS, high-quality CRS datasets are ve…
Improving Conversational Recommendation Systems via Counterfactual Data Simulation
Xiaolei Wang, Kun Zhou, Xinyu Tang +4
Conversational recommender systems (CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developi…
Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models
Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao +2
The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conv…
A Survey of Large Language Models
Wayne Xin Zhao, Kun Zhou, Junyi Li +19
Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for compre…
Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning
Xiaolei Wang, Kun Zhou, Ji-Rong Wen +1
Conversational recommender systems (CRS) aim to proactively elicit user preference and recommend high-quality items through natural language conversations. Typically, a CRS consist…
CRSLab: An Open-Source Toolkit for Building Conversational Recommender System
Kun Zhou, Xiaolei Wang, Yuanhang Zhou +5
In recent years, conversational recommender system (CRS) has received much attention in the research community. However, existing studies on CRS vary in scenarios, goals and techni…