1 citations · 2 across the 21 of their papers we have counts for
11 papers · 1 filter
An Information-theoretic Propagation Denoising and Fusion Framework for Fake News Detection
Mengyang Chen, Lingwei Wei, Wei Zhou +1
Incomplete propagation data significantly hinders robust fake news detection. Recent approaches leverage large language models to simulate missing user interactions via role-playin…
Propagation Structure-Semantic Transfer Learning for Robust Fake News Detection
Mengyang Chen, Lingwei Wei, Han Cao +3
Fake news generally refers to false information that is spread deliberately to deceive people, which has detrimental social effects. Existing fake news detection methods primarily…
Diagnosing Retrieval Bias Under Multiple In-Context Knowledge Updates in Large Language Models
Boyu Qiao, Sean Guo, Xian Yang +4
LLMs are widely used in knowledge-intensive tasks where the same fact may be revised multiple times within context. Unlike prior work focusing on one-shot updates or single conflic…
Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agents
Yiting Shen, Kun Li, Wei Zhou +1
Large Language Model (LLM)-based agents are increasingly deployed for complex, tool-based tasks where long-term memory is critical to driving actions. Existing benchmarks, however,…
Enhancing Multi-Hop Fact Verification with Structured Knowledge-Augmented Large Language Models
Han Cao, Lingwei Wei, Wei Zhou +1
The rapid development of social platforms exacerbates the dissemination of misinformation, which stimulates the research in fact verification. Recent studies tend to leverage seman…
An Information-theoretic Multi-task Representation Learning Framework for Natural Language Understanding
Dou Hu, Lingwei Wei, Wei Zhou +1
This paper proposes a new principled multi-task representation learning framework (InfoMTL) to extract noise-invariant sufficient representations for all tasks. It ensures sufficie…
Fine-Grained Behavior Simulation with Role-Playing Large Language Model on Social Media
Kun Li, Chenwei Dai, Wei Zhou +1
Large language models (LLMs) have demonstrated impressive capabilities in role-playing tasks. However, there is limited research on whether LLMs can accurately simulate user behavi…
Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction
Jinchuan Zhang, Yan Zhou, Yaxin Liu +2
Automated red teaming is an effective method for identifying misaligned behaviors in large language models (LLMs). Existing approaches, however, often focus primarily on improving…
MaskMoE: Boosting Token-Level Learning via Routing Mask in Mixture-of-Experts
Zhenpeng Su, Zijia Lin, Xue Bai +8
Scaling the size of a model enhances its capabilities but significantly increases computation complexity. Mixture-of-Experts models (MoE) address the issue by allowing model size t…
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis
Yaxin Liu, Yan Zhou, Ziming Li +4
As an important multimodal sentiment analysis task, Joint Multimodal Aspect-Sentiment Analysis (JMASA), aiming to jointly extract aspect terms and their associated sentiment polari…
Structured Probabilistic Coding
Dou Hu, Lingwei Wei, Yaxin Liu +2
This paper presents a new supervised representation learning framework, namely structured probabilistic coding (SPC), to learn compact and informative representations from input re…