2 citations · 2 across the 11 of their papers we have counts for
12 papers
FREAK: A Fine-grained Hallucination Evaluation Benchmark for Advanced MLLMs
Zhihan Yin, Jianxin Liang, Yueqian Wang +3
Multimodal Large Language Models (MLLMs) suffer from hallucinations. Existing hallucination evaluation benchmarks are often limited by over-simplified tasks leading to saturated me…
MMDuet2: Enhancing Proactive Interaction of Video MLLMs with Multi-Turn Reinforcement Learning
Yueqian Wang, Songxiang Liu, Disong Wang +4
Recent advances in video multimodal large language models (Video MLLMs) have significantly enhanced video understanding and multi-modal interaction capabilities. While most existin…
K-frames: Scene-Driven Any-k Keyframe Selection for long video understanding
Yifeng Yao, Yike Yun, Jing Wang +6
Multimodal Large Language Models (MLLMs) have demonstrated significant capabilities in image understanding, but long-video are constrained by context windows and computational cost…
Do LLMs "Feel"? Emotion Circuits Discovery and Control
Chenxi Wang, Yixuan Zhang, Ruiji Yu +8
As the demand for emotional intelligence in large language models (LLMs) grows, a key challenge lies in understanding the internal mechanisms that give rise to emotional expression…
Beyond Isolated Facts: Synthesizing Narrative and Grounded Supervision for VideoQA
Jianxin Liang, Tan Yue, Yuxuan Wang +4
The performance of Video Question Answering (VideoQA) models is fundamentally constrained by the nature of their supervision, which typically consists of isolated, factual question…
ProactiveVideoQA: A Comprehensive Benchmark Evaluating Proactive Interactions in Video Large Language Models
Yueqian Wang, Xiaojun Meng, Yifan Wang +2
With the growing research focus on multimodal dialogue systems, the capability for proactive interaction is gradually gaining recognition. As an alternative to conventional turn-by…
Latent Preference Coding: Aligning Large Language Models via Discrete Latent Codes
Zhuocheng Gong, Jian Guan, Wei Wu +2
Large language models (LLMs) have achieved remarkable success, yet aligning their generations with human preferences remains a critical challenge. Existing approaches to preference…
Shorten After You're Right: Lazy Length Penalties for Reasoning RL
Danlong Yuan, Tian Xie, Shaohan Huang +5
Large reasoning models, such as OpenAI o1 or DeepSeek R1, have demonstrated remarkable performance on reasoning tasks but often incur a long reasoning path with significant memory…
ReasVQA: Advancing VideoQA with Imperfect Reasoning Process
Jianxin Liang, Xiaojun Meng, Huishuai Zhang +3
Video Question Answering (VideoQA) is a challenging task that requires understanding complex visual and temporal relationships within videos to answer questions accurately. In this…
VideoLLM Knows When to Speak: Enhancing Time-Sensitive Video Comprehension with Video-Text Duet Interaction Format
Yueqian Wang, Xiaojun Meng, Yuxuan Wang +4
Recent researches on video large language models (VideoLLM) predominantly focus on model architectures and training datasets, leaving the interaction format between the user and th…
Understanding Multimodal Hallucination with Parameter-Free Representation Alignment
Yueqian Wang, Jianxin Liang, Yuxuan Wang +2
Hallucination is a common issue in Multimodal Large Language Models (MLLMs), yet the underlying principles remain poorly understood. In this paper, we investigate which components…
Evidence-Enhanced Triplet Generation Framework for Hallucination Alleviation in Generative Question Answering
Haowei Du, Huishuai Zhang, Dongyan Zhao
To address the hallucination in generative question answering (GQA) where the answer can not be derived from the document, we propose a novel evidence-enhanced triplet generation f…