We often talk about reasoning abilities and context tracking in large language models. What underlying mechanisms enable them to maintain coherence across multiple sentences? Is multi-head attention sufficient, or do we need additional architectures to truly "understand" the thread? I’d love to hear your experiences and perspectives on current limitations and possible improvement paths.
Can large language models truly understand context when generating text?
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What role does the input context window size play in a model's ability to follow a narrative thread across multiple paragraphs, and are there specific fine-tuning techniques to improve this coherence?
Thanks for the detailed question! From my experience, while Multi-Head Attention captures local context well, longer coherence often requires additional memory or retrieval mechanisms—have you experimented with Transformer-XL or Retrieval-Augmented Generation yet?