Large Language Model
Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
Recently, various new methods have been proposed to adapt closed LLMs to private data without leaking private information to third parties and/or the LLM provider.
Unified Lexical Representation for Interpretable Visual-Language Alignment
Visual-Language Alignment (VLA) has gained a lot of attention since CLIP's groundbreaking work.
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate
Generalization remains a central challenge in machine learning.
Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens
Pre-trained large language models based on Transformers have demonstrated remarkable in-context learning (ICL) abilities. With just a few demonstration examples, the models can implement new tasks without any parameter updates. However, it is still an open question to understand the mechanism of ICL.
Slot-VLM: Object-Event Slots for Video-Language Modeling
Video-Language Models (VLMs), powered by the advancements in Large Language Models (LLMs), are charting new frontiers in video understanding.
