7 citations · 7 across the 1 of their papers we have counts for
9 papers
MM-LIMA: Less Is More for Alignment in Multi-Modal Datasets
Lai Wei, Xiaozhe Li, Zihao Jiang +2
Multimodal large language models are typically trained in two stages: first pre-training on image-text pairs, and then fine-tuning using supervised vision-language instruction data…
Provable Training Data Identification for Large Language Models
Zhenlong Liu, Hao Zeng, Weiran Huang +1
Identifying training data of large-scale models is critical for copyright litigation, privacy auditing, and ensuring fair evaluation. However, existing works typically treat this t…
Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management
Lai Wei, Zhen Ying, Muyang He +9
Diabetes is a chronic disease with a significant global health burden, requiring multi-stakeholder collaboration for optimal management. Large language models (LLMs) have shown pro…
Information-Theoretic Perspectives on Optimizers
Zhiquan Tan, Weiran Huang
The interplay of optimizers and architectures in neural networks is complicated and hard to understand why some optimizers work better on some specific architectures. In this paper…
Exploring Information-Theoretic Metrics Associated with Neural Collapse in Supervised Training
Kun Song, Zhiquan Tan, Bochao Zou +3
In this paper, we introduce matrix entropy as an analytical tool for studying supervised learning, investigating the information content of data representations and classification…
FinLLM-B: When Large Language Models Meet Financial Breakout Trading
Kang Zhang, Osamu Yoshie, Lichao Sun +1
Trading range breakout is a key method in the technical analysis of financial trading, widely employed by traders in financial markets such as stocks, futures, and foreign exchange…