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20152026
most citedA Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

2.1k citations · 3.1k across the 75 of their papers we have counts for

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12 papers · 1 filter

cs.CV2026

Proactive Physical AI through Continuous Observation and Timely Intervention

Zhenhua Yang, Jiafeng Liang, Xingyu Zhou +26

Physical AI is commonly formulated around an explicitly specified task: given a goal or instruction, a system perceives, reasons, and acts. In many real-world settings, however, im…

cs.CV2026

MT-Web2Code: Benchmarking Coding Agents on Multi-Turn Regional Reconstruction and Localized Modification

Qiming Li, Shujie Hu, Haohan Liu +3

Recent advances in Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities in web UI generation. However, existing benchmarks predominantly focus on single-t…

cs.CV2026

CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering

Qiming Li, Zekai Ye, Xiaocheng Feng +9

Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviate from visual information, l…

cs.CV2025

Causal Tracing of Object Representations in Large Vision Language Models: Mechanistic Interpretability and Hallucination Mitigation

Qiming Li, Zekai Ye, Xiaocheng Feng +3

Despite the remarkable advancements of Large Vision-Language Models (LVLMs), the mechanistic interpretability remains underexplored. Existing analyses are insufficiently comprehens…

cs.CV2025

Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering

Qiming Li, Xiaocheng Feng, Yixuan Ma +4

Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) demonstrate strong reasoning capabilities, yet their performance in English significantly outperforms that in…

cs.CV2025

CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models

Qiming Li, Zekai Ye, Xiaocheng Feng +8

Although Large Vision-Language Models (LVLMs) have demonstrated powerful capabilities in interpreting visual information, they frequently produce content that deviates from visual…

cs.CV2024

Cross-Lingual Text-Rich Visual Comprehension: An Information Theory Perspective

Xinmiao Yu, Xiaocheng Feng, Yun Li +10

Recent Large Vision-Language Models (LVLMs) have shown promising reasoning capabilities on text-rich images from charts, tables, and documents. However, the abundant text within su…

cs.CV2024

CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models

Zihui Cheng, Qiguang Chen, Jin Zhang +5

Large Vision-Language Models (LVLMs) have recently demonstrated amazing success in multi-modal tasks, including advancements in Multi-modal Chain-of-Thought (MCoT) reasoning. Despi…

cs.CV2024★ 1 cited

Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models

Weihong Zhong, Xiaocheng Feng, Liang Zhao +6

Though advanced in understanding visual information with human languages, Large Vision-Language Models (LVLMs) still suffer from multimodal hallucinations. A natural concern is tha…

cs.CV2023

Emage: Non-Autoregressive Text-to-Image Generation

Zhangyin Feng, Runyi Hu, Liangxin Liu +7

Autoregressive and diffusion models drive the recent breakthroughs on text-to-image generation. Despite their huge success of generating high-realistic images, a common shortcoming…

cs.CV2023

Improved Visual Story Generation with Adaptive Context Modeling

Zhangyin Feng, Yuchen Ren, Xinmiao Yu +4

Diffusion models developed on top of powerful text-to-image generation models like Stable Diffusion achieve remarkable success in visual story generation. However, the best-perform…

cs.CV2023★ 6 cited

STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-training

Weihong Zhong, Mao Zheng, Duyu Tang +4

Although large-scale video-language pre-training models, which usually build a global alignment between the video and the text, have achieved remarkable progress on various downstr…