activity
20242026
most citedRe-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors

9 citations · 9 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement

Qimin Zhong, Hao Liao, Haiming Qin +4

Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervis…

cs.AI2025

Understanding and Enhancing the Planning Capability of Language Models via Multi-Token Prediction

Qimin Zhong, Hao Liao, Siwei Wang +4

Large Language Models (LLMs) have achieved impressive performance across diverse tasks but continue to struggle with learning transitive relations, a cornerstone for complex planni…

cs.IR2025

Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View

Hao Liao, Jiwei Zhang, Jianxun Lian +7

Recommender systems based on Large Language Models (LLMs) are often plagued by hallucinations of out-of-domain (OOD) items. To address this, we propose RecLM, a unified framework t…

cond-mat.dis-nn2024

Exploring Loss Landscapes through the Lens of Spin Glass Theory

Hao Liao, Wei Zhang, Zhanyi Huang +5

In the past decade, significant strides in deep learning have led to numerous groundbreaking applications. Despite these advancements, the understanding of the high generalizabilit…

cs.CL20249 cited

Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors

Guanghua Li, Wensheng Lu, Wei Zhang +5

The proliferation of fake news has had far-reaching implications on politics, the economy, and society at large. While Fake news detection methods have been employed to mitigate th…