38 citations · 52 across the 24 of their papers we have counts for
10 papers · 1 filter
Latent Sketchpad: Sketching Visual Thoughts to Elicit Multimodal Reasoning in MLLMs
Huanyu Zhang, Wenshan Wu, Chengzu Li +9
While Multimodal Large Language Models (MLLMs) excel at visual understanding, they often struggle in complex scenarios that require visual planning and imagination. Inspired by how…
Deforming Videos to Masks: Flow Matching for Referring Video Segmentation
Zanyi Wang, Dengyang Jiang, Liuzhuozheng Li +6
Referring Video Object Segmentation (RVOS) requires segmenting specific objects in a video guided by a natural language description. The core challenge of RVOS is to anchor abstrac…
Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models
Ej Zhou, Caiqi Zhang, Tiancheng Hu +4
Confidence calibration, the alignment of a model's predicted confidence with its actual accuracy, is crucial for the reliable deployment of Large Language Models (LLMs). However, t…
Lost in Embeddings: Information Loss in Vision-Language Models
Wenyan Li, Raphael Tang, Chengzu Li +3
Vision--language models (VLMs) often process visual inputs through a pretrained vision encoder, followed by a projection into the language model's embedding space via a connector c…
11Plus-Bench: Demystifying Multimodal LLM Spatial Reasoning with Cognitive-Inspired Analysis
Chengzu Li, Wenshan Wu, Huanyu Zhang +6
For human cognitive process, spatial reasoning and perception are closely entangled, yet the nature of this interplay remains underexplored in the evaluation of multimodal large la…
Enriching Patent Claim Generation with European Patent Dataset
Lekang Jiang, Chengzu Li, Stephan Goetz
Drafting patent claims is time-intensive, costly, and requires professional skill. Therefore, researchers have investigated large language models (LLMs) to assist inventors in writ…
LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generations
Caiqi Zhang, Xiaochen Zhu, Chengzu Li +2
Hallucination remains a major challenge for the safe and trustworthy deployment of large language models (LLMs) in factual content generation. Prior work has explored confidence es…
Visual Planning: Let's Think Only with Images
Yi Xu, Chengzu Li, Han Zhou +4
Recent advancements in Large Language Models (LLMs) and their multimodal extensions (MLLMs) have substantially enhanced machine reasoning across diverse tasks. However, these model…
Scaling and Beyond: Advancing Spatial Reasoning in MLLMs Requires New Recipes
Huanyu Zhang, Chengzu Li, Wenshan Wu +8
Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in general vision-language tasks. However, recent studies have exposed critical limitations in the…
Imagine while Reasoning in Space: Multimodal Visualization-of-Thought
Chengzu Li, Wenshan Wu, Huanyu Zhang +5
Chain-of-Thought (CoT) prompting has proven highly effective for enhancing complex reasoning in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs). Yet, it s…