Summary of Future Token Prediction — Causal Language Modelling with Per-token Semantic State Vector For Multi-token Prediction, by Nicholas Walker
Future Token Prediction – Causal Language Modelling with Per-Token Semantic State Vector for Multi-Token Prediction
by Nicholas Walker
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper explores the limitations of current causal decoder-only transformer models, such as Generative Pre-trained Transformers (GPT), which excel in generative language modelling. These models are trained to predict the next token based solely on its previous tokens, achieving remarkable performance. However, their focus is mainly on predicting individual tokens, resulting in top-layer embedding vectors that prioritize local token features. The authors hypothesize that generating embedding vectors at each token position can better capture the overall meaning of longer sequences by considering multiple future tokens. This idea is inspired by recent studies matching brain scans with deep language models, suggesting that humans predict upcoming words while considering multiple future tokens. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks into how we can make AI language tools better at understanding longer pieces of text. Current AI models are great at predicting the next word in a sentence, but they’re only thinking about one word ahead. This paper asks if AI could do better by considering more than just the next word. The idea comes from studies that compared human brain activity with AI language models and found that humans think about multiple future words when reading or listening to text. |
Keywords
» Artificial intelligence » Decoder » Embedding » Gpt » Token » Transformer
