2 citations · 2 across the 4 of their papers we have counts for
5 papers
Can Large Vision-Language Models Understand Multimodal Sarcasm?
Xinyu Wang, Yue Zhang, Liqiang Jing
Sarcasm is a complex linguistic phenomenon that involves a disparity between literal and intended meanings, making it challenging for sentiment analysis and other emotion-sensitive…
LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research
Shuo Yan, Ruochen Li, Ziming Luo +11
Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery. However, their capability in the fundamental yet crucial task of reprodu…
Speech Recognition on TV Series with Video-guided Post-ASR Correction
Haoyuan Yang, Yue Zhang, Liqiang Jing +1
Automatic Speech Recognition (ASR) has achieved remarkable success with deep learning, driving advancements in conversational artificial intelligence, media transcription, and assi…
Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization
Yue Zhang, Liqiang Jing, Vibhav Gogate
We introduce a new task called Defeasible Visual Entailment (DVE), where the goal is to allow the modification of the entailment relationship between an image premise and a text hy…
A Unified Hallucination Mitigation Framework for Large Vision-Language Models
Yue Chang, Liqiang Jing, Xiaopeng Zhang +1
Hallucination is a common problem for Large Vision-Language Models (LVLMs) with long generations which is difficult to eradicate. The generation with hallucinations is partially in…