Summary of Dynamic Vocabulary Pruning in Early-exit Llms, by Jort Vincenti et al.
Dynamic Vocabulary Pruning in Early-Exit LLMs
by Jort Vincenti, Karim Abdel Sadek, Joan Velja, Matteo Nulli, Metod Jazbec
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed approach for improving the efficiency of large language model (LLM) inference involves dynamically pruning the vocabulary at test time, allowing for faster and more cost-effective next token prediction. By reducing the size of the vocabulary used throughout the forward pass, confidence estimation becomes less computationally expensive, achieving better performance in early-exit LLMs while maintaining competitive results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make big language models work faster by reducing the number of words they need to consider when predicting what comes next. This makes it easier and cheaper to use these models for tasks like language translation or text summarization, without sacrificing performance. |
Keywords
» Artificial intelligence » Inference » Large language model » Pruning » Summarization » Token » Translation
