Summary of Do Language Models Understand Time?, by Xi Ding and Lei Wang
Do Language Models Understand Time?
by Xi Ding, Lei Wang
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have transformed video-based computer vision applications, including action recognition, anomaly detection, and video summarization. To tackle unique challenges posed by videos, current approaches rely on pretrained video encoders to extract spatiotemporal features and text encoders to capture semantic meaning. However, the ability of LLMs to understand time and reason about temporal relationships remains unexplored. This work examines the role of LLMs in video processing, identifying limitations in their interaction with pretrained encoders. We reveal gaps in modeling long-term dependencies and abstract temporal concepts such as causality and event progression. Additionally, we analyze challenges posed by existing video datasets, including biases, lack of temporal annotations, and domain-specific limitations. To address these gaps, we explore co-evolution of LLMs and encoders, enriched datasets with explicit temporal labels, and innovative architectures for integrating spatial, temporal, and semantic reasoning. By advancing the temporal comprehension of LLMs, our work aims to unlock their potential in video analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are special kinds of computers that can understand videos. They’ve been very good at recognizing actions, finding weird things, and making short summaries of videos. But there’s something missing – they don’t really understand time. This paper is about how LLMs work with videos and what they’re not so good at when it comes to understanding time. It talks about the problems with using these models with video data and how we can make them better. The goal is to help LLMs be even more helpful in analyzing videos. |
Keywords
» Artificial intelligence » Anomaly detection » Spatiotemporal » Summarization
