activity
20232026
most citedThe Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential Recommendation

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL2026

On Reasoning Behind Next Occupation Recommendation

Shan Dong, Palakorn Achananuparp, Hieu Hien Mai +3

In this work, we develop a novel reasoning approach to enhance the performance of large language models (LLMs) in future occupation prediction. In this approach, a reason generator…

cs.CL2026

PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents

Minjia Wang, Yunfeng Wang, Xiao Ma +9

Digital footprints (records of individuals' interactions with digital systems) are essential for studying behavior, developing personalized applications, and training machine learn…

cs.IR20241 cited

The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential Recommendation

Lei Wang, Ee-Peng Lim

Large language models (LLMs) have shown excellent performance on various NLP tasks. To use LLMs as strong sequential recommenders, we explore the in-context learning approach to se…

cs.CV2023

Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites

Lei Wang, Jiabang He, Shenshen Li +2

Large language models (LLMs) have shown remarkable performance in natural language processing (NLP) tasks. To comprehend and execute diverse human instructions over image data, ins…

cs.CL2023

LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay

Yihuai Lan, Zhiqiang Hu, Lei Wang +6

This paper explores the open research problem of understanding the social behaviors of LLM-based agents. Using Avalon as a testbed, we employ system prompts to guide LLM agents in…