2 citations · 2 across the 6 of their papers we have counts for
6 papers
DeployBench: Benchmarking LLM Agents for Research Artifact Deployment
Yuanli Wang, Yaoyao Qian, Yue Zhang +8
LLM agents have made rapid progress on software engineering and ML research tasks, but these advances often assume access to a working runnable environment. For research artifacts…
SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
Xiangyi Li, Yimin Liu, Wenbo Chen +75
Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to m…
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…
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…