10 citations · 19 across the 22 of their papers we have counts for
23 papers · 1 filter
From Signals to Transfer: A Factorised Study of Probe-Based Uncertainty Estimation in Large Language Models
Ponhvoan Srey, Xiaobao Wu, Cong-Duy Nguyen +3
Probe-based uncertainty estimation (UE) has emerged as a prominent approach to detect hallucinations in Large Language Models (LLMs) by learning uncertainty from internal model sig…
Learning Uncertainty from Sequential Internal Dispersion in Large Language Models
Ponhvoan Srey, Xiaobao Wu, Cong-Duy Nguyen +1
Uncertainty estimation is a promising approach to detect hallucinations in large language models (LLMs). Recent approaches commonly depend on model internal states to estimate unce…
Unsupervised Hallucination Detection by Inspecting Reasoning Processes
Ponhvoan Srey, Xiaobao Wu, Anh Tuan Luu
Unsupervised hallucination detection aims to identify hallucinated content generated by large language models (LLMs) without relying on labeled data. While unsupervised methods hav…
Read as You See: Guiding Unimodal LLMs for Low-Resource Explainable Harmful Meme Detection
Fengjun Pan, Xiaobao Wu, Tho Quan +1
Detecting harmful memes is crucial for safeguarding the integrity and harmony of online environments, yet existing detection methods are often resource-intensive, inflexible, and l…
Aspect-Based Summarization with Self-Aspect Retrieval Enhanced Generation
Yichao Feng, Shuai Zhao, Yueqiu Li +3
Aspect-based summarization aims to generate summaries tailored to specific aspects, addressing the resource constraints and limited generalizability of traditional summarization ap…
Full-Step-DPO: Self-Supervised Preference Optimization with Step-wise Rewards for Mathematical Reasoning
Huimin Xu, Xin Mao, Feng-Lin Li +4
Direct Preference Optimization (DPO) often struggles with long-chain mathematical reasoning. Existing approaches, such as Step-DPO, typically improve this by focusing on the first…