Summary of Contextual Position Encoding: Learning to Count What’s Important, by Olga Golovneva et al.
Contextual Position Encoding: Learning to Count What’s Important
by Olga Golovneva, Tianlu Wang, Jason Weston, Sainbayar Sukhbaatar
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel attention mechanism is proposed in this paper to enable Large Language Models (LLMs) to address positions based on context. The existing attention mechanism is order-invariant, but incorporating position encoding (PE) allows for addressing by position. However, current PE methods rely on token counts and cannot generalize to higher levels of abstraction. To address this limitation, the authors introduce Contextual Position Encoding (CoPE), which conditions positions on context by incrementing position only on certain tokens determined by the model. This enables more general position addressing, such as attending to a specific word or sentence. The paper demonstrates that CoPE can solve tasks where popular position embeddings fail and improves perplexity on language modeling and coding tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes a new way for Large Language Models (LLMs) to understand the context of words and sentences. Right now, these models are good at understanding the relationships between words in a sentence, but they can’t identify specific positions or levels of abstraction, like attending to a particular word or sentence. To fix this, the authors create a new method called Contextual Position Encoding (CoPE), which helps the model understand context and position better. This new method is tested on different tasks and shows that it can solve problems where other methods fail. |
Keywords
» Artificial intelligence » Attention » Perplexity » Token
