1 citations · 1 across the 5 of their papers we have counts for
7 papers
Simple o3: Towards Interleaved Vision-Language Reasoning
Ye Wang, Qianglong Chen, Zejun Li +4
Multimodal Large Language Models (MLLMs) have shown impressive performance on vision-language tasks, but their long Chain-of-Thought (CoT) capabilities in multimodal scenarios rema…
CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention
Zekai Ye, Qiming Li, Xiaocheng Feng +10
Large Vision-Language Models (LVLMs) have demonstrated impressive multimodal abilities but remain prone to multilingual object hallucination, with a higher likelihood of generating…
CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning
Yangfan Ye, Xiaocheng Feng, Zekun Yuan +11
Current large language models (LLMs) often exhibit imbalanced multilingual capabilities due to their English-centric training corpora. To address this, existing fine-tuning approac…
Safe Delta: Consistently Preserving Safety when Fine-Tuning LLMs on Diverse Datasets
Ning Lu, Shengcai Liu, Jiahao Wu +5
Large language models (LLMs) have shown great potential as general-purpose AI assistants across various domains. To fully leverage this potential in specific applications, many com…
Is PRM Necessary? Problem-Solving RL Implicitly Induces PRM Capability in LLMs
Zhangying Feng, Qianglong Chen, Ning Lu +6
The development of reasoning capabilities represents a critical frontier in large language models (LLMs) research, where reinforcement learning (RL) and process reward models (PRMs…
Enhancing Non-English Capabilities of English-Centric Large Language Models through Deep Supervision Fine-Tuning
Wenshuai Huo, Xiaocheng Feng, Yichong Huang +9
Large language models (LLMs) have demonstrated significant progress in multilingual language understanding and generation. However, due to the imbalance in training data, their cap…