1 citations · 2 across the 9 of their papers we have counts for
10 papers
CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images
Chengqi Duan, Kaiyue Sun, Rongyao Fang +10
Recent advances in Large Language Models (LLMs) and Vision Language Models (VLMs) have shown significant progress in mathematical reasoning, yet they still face a critical bottlene…
TASO: Task-Aligned Sparse Optimization for Parameter-Efficient Model Adaptation
Daiye Miao, Yufang Liu, Jie Wang +6
LoRA has become one of the most widely used parameter-efficient fine-tuning methods due to its simplicity and effectiveness. However, numerous studies have shown that LoRA often in…
BMMR: A Large-Scale Bilingual Multimodal Multi-Discipline Reasoning Dataset
Zhiheng Xi, Guanyu Li, Yutao Fan +13
In this paper, we introduce BMMR, a large-scale bilingual, multimodal, multi-disciplinary reasoning dataset for the community to develop and evaluate large multimodal models (LMMs)…
On Support Samples of Next Word Prediction
Yuqian Li, Yupei Du, Yufang Liu +3
Language models excel in various tasks by making complex decisions, yet understanding the rationale behind these decisions remains a challenge. This paper investigates \emph{data-c…
The Role of Visual Modality in Multimodal Mathematical Reasoning: Challenges and Insights
Yufang Liu, Yao Du, Tao Ji +6
Recent research has increasingly focused on multimodal mathematical reasoning, particularly emphasizing the creation of relevant datasets and benchmarks. Despite this, the role of…
Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models
Yufang Liu, Tao Ji, Changzhi Sun +2
Large Vision-Language Models (LVLMs) have achieved impressive performance, yet research has pointed out a serious issue with object hallucinations within these models. However, the…
EAGLE: Elevating Geometric Reasoning through LLM-empowered Visual Instruction Tuning
Zhihao Li, Yao Du, Yang Liu +6
Multi-modal Large Language Models (MLLMs) have advanced greatly in general tasks. However, they still face challenges in geometric reasoning, a task that requires synergistic integ…
Unlearning with Fisher Masking
Yufang Liu, Changzhi Sun, Yuanbin Wu +1
Machine unlearning aims to revoke some training data after learning in response to requests from users, model developers, and administrators. Most previous methods are based on dir…
Few Clean Instances Help Denoising Distant Supervision
Yufang Liu, Ziyin Huang, Yijun Wang +5
Existing distantly supervised relation extractors usually rely on noisy data for both model training and evaluation, which may lead to garbage-in-garbage-out systems. To alleviate…
Generating CCG Categories
Yufang Liu, Tao Ji, Yuanbin Wu +1
Previous CCG supertaggers usually predict categories using multi-class classification. Despite their simplicity, internal structures of categories are usually ignored. The rich sem…