4 papers
The Shape of Time: Video-Token Contrast for Temporal Understanding in VideoLMs
Yumeng Shi, Quanyu Long, Yin Wu +1
Seeing frames in order does not mean representing time. Modern VideoLMs receive ordered video streams, yet their main supervision acts on generated text rather than video-token rep…
Causality Matters: How Temporal Information Emerges in Video Language Models
Yumeng Shi, Quanyu Long, Yin Wu +1
Video language models (VideoLMs) have made significant progress in multimodal understanding. However, temporal understanding, which involves identifying event order, duration, and…
Visual-RAG: Benchmarking Text-to-Image Retrieval Augmented Generation for Visual Knowledge Intensive Queries
Yin Wu, Quanyu Long, Jing Li +2
Retrieval-augmented generation (RAG) is a paradigm that augments large language models (LLMs) with external knowledge to tackle knowledge-intensive question answering. While severa…
Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context Learning
Quanyu Long, Yin Wu, Wenya Wang +1
In-context Learning (ICL) has emerged as a powerful capability alongside the development of scaled-up large language models (LLMs). By instructing LLMs using few-shot demonstrative…