3 citations · 5 across the 8 of their papers we have counts for
9 papers
Disentangling Deception and Hallucination Failures in LLMs
Haolang Lu, Hongrui Peng, WeiYe Fu +5
Failures in large language models (LLMs) are often analyzed from a behavioral perspective, where incorrect outputs in factual question answering are commonly associated with missin…
Diagnosing Knowledge Conflict in Multimodal Long-Chain Reasoning
Jing Tang, Kun Wang, Haolang Lu +7
Multimodal large language models (MLLMs) in long chain-of-thought reasoning often fail when different knowledge sources provide conflicting signals. We formalize these failures und…
CSR-Bench: A Benchmark for Evaluating the Cross-modal Safety and Reliability of MLLMs
Yuxuan Liu, Yuntian Shi, Kun Wang +2
Multimodal large language models (MLLMs) enable interaction over both text and images, but their safety behavior can be driven by unimodal shortcuts instead of true joint intent un…
AR-MAP: Are Autoregressive Large Language Models Implicit Teachers for Diffusion Large Language Models?
Liang Lin, Feng Xiong, Zengbin Wang +5
Diffusion Large Language Models (DLLMs) have emerged as a powerful alternative to autoregressive models, enabling parallel token generation across multiple positions. However, pref…
V-ITI: Mitigating Hallucinations in Multimodal Large Language Models via Visual Inference-Time Intervention
Nan Sun, Zhenyu Zhang, Xixun Lin +8
Multimodal Large Language Models (MLLMs) excel in numerous vision-language tasks yet suffer from hallucinations, producing content inconsistent with input visuals, that undermine r…
Design of intelligent proofreading system for English translation based on CNN and BERT
Feijun Liu, Huifeng Wang, Kun Wang +1
Since automatic translations can contain errors that require substantial human post-editing, machine translation proofreading is essential for improving quality. This paper propose…
GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning
Yue Liu, Shengfang Zhai, Mingzhe Du +9
To enhance the safety of VLMs, this paper introduces a novel reasoning-based VLM guard model dubbed GuardReasoner-VL. The core idea is to incentivize the guard model to deliberativ…
Grad-ECLIP: Gradient-based Visual and Textual Explanations for CLIP
Chenyang Zhao, Kun Wang, Janet H. Hsiao +1
Significant progress has been achieved on the improvement and downstream usages of the Contrastive Language-Image Pre-training (CLIP) vision-language model, while less attention is…
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering
Yuan Sui, Yufei He, Nian Liu +3
Large Language Models (LLMs) are often challenged by generating erroneous or hallucinated responses, especially in complex reasoning tasks. Leveraging Knowledge Graphs (KGs) as ext…