"Do Language Models Understand Time?"

Time is the secret ingredient LLMs haven't mastered in video understanding yet.

This paper examines if LLMs truly understand temporal relationships in videos, revealing limitations in their ability to process long-term dependencies and abstract temporal concepts.

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https://arxiv.org/abs/2412.13845

🤔 Original Problem:

While LLMs excel at video tasks like action recognition and anomaly detection, their actual comprehension of time-based relationships and temporal dynamics remains unclear and unproven.

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🔍 Solution in this Paper:

→ The paper critically analyzes how LLMs interact with pretrained video encoders to process temporal information

→ It identifies gaps in current approaches, particularly in modeling long-term dependencies and abstract temporal concepts

→ The research examines limitations in existing video datasets, including lack of temporal annotations and biases toward short-term patterns

→ Proposes pathways for joint training of encoders and LLMs, enriched datasets with explicit temporal labels, and innovative architectures

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💡 Key Insights:

→ Current LLMs rely heavily on pretrained encoders that focus on short-term patterns

→ Video datasets lack proper temporal annotations and diverse scenarios

→ Joint optimization of encoders and LLMs is crucial for better temporal understanding

→ Multimodal alignment between visual and semantic features needs improvement

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📊 Results:

→ Analysis spans 66 video-LLMs from 2024, revealing limitations in temporal reasoning

→ Most models achieve high accuracy on short-term tasks but struggle with long-term dependencies

→ Cross-attention (35%) and projection layers (30%) are dominant fusion mechanisms