4 papers
When Models Judge Themselves: Unsupervised Self-Evolution for Multimodal Reasoning
Zhengxian Wu, Kai Shi, Chuanrui Zhang +10
Recent progress in multimodal large language models has led to strong performance on reasoning tasks, but these improvements largely rely on high-quality annotated data or teacher-…
What Matters in LLM-Based Feature Extractor for Recommender? A Systematic Analysis of Prompts, Models, and Adaptation
Kainan Shi, Peilin Zhou, Ge Wang +2
Using Large Language Models (LLMs) to generate semantic features has been demonstrated as a powerful paradigm for enhancing Sequential Recommender Systems (SRS). This typically inv…
DaMo: Data Mixing Optimizer in Fine-tuning Multimodal LLMs for Mobile Phone Agents
Kai Shi, Jun Yang, Ni Yang +6
Mobile Phone Agents (MPAs) have emerged as a promising research direction due to their broad applicability across diverse scenarios. While Multimodal Large Language Models (MLLMs)…
ReviewInstruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models
Jiangxu Wu, Cong Wang, TianHuang Su +10
The effectiveness of large language models (LLMs) in conversational AI is hindered by their reliance on single-turn supervised fine-tuning (SFT) data, which limits contextual coher…