Summary of Implicit Geometry Of Next-token Prediction: From Language Sparsity Patterns to Model Representations, by Yize Zhao et al.
Implicit Geometry of Next-token Prediction: From Language Sparsity Patterns to Model Representations
by Yize Zhao, Tina Behnia, Vala Vakilian, Christos Thrampoulidis
First submitted to arxiv on: 27 Aug 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 how training large language models using next-token prediction (NTP) affects their representation geometry. The authors frame NTP as soft-label classification with sparse probabilistic label vectors, coupled with an analytical approximation for generating context embeddings. This framework links NTP to rank-constrained optimization in the logit domain, enabling analysis of word and context embedding geometry. In large spaces, NTP favors learning logits with a sparse plus low-rank structure, capturing co-occurrence frequencies and subspace collapse phenomena. The authors validate their findings on synthetic and real language datasets, outlining potential research directions for deepening understanding of NTP’s influence on linguistic patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how big language models learn to recognize patterns in text. It shows that when we train these models using a technique called next-token prediction (NTP), they develop a special structure in their internal representations. This structure is related to the way words and phrases appear together in text, and it can help us better understand how language works. |
Keywords
» Artificial intelligence » Classification » Embedding » Logits » Optimization » Token
