Efficient Inference in Large Language Models

The field of natural language processing is moving towards more efficient inference in large language models. Researchers are exploring ways to reduce computational costs while maintaining model quality, such as predicting activation patterns, using tiny language models, and developing hybrid early-exit algorithms. Noteworthy papers include:

  • A study on clustering-based activation pattern compression, which achieves up to 79.34% clustering precision and preserves model quality.
  • A proposed hybrid early-exit algorithm that aligns intermediate layer representations with the output layer, reducing inference costs without compromising accuracy.
  • A method for converting continuous embeddings into binary representations using feature-wise thresholding, which enhances performance across various features.

Sources

A Sparsity Predicting Approach for Large Language Models via Activation Pattern Clustering

Tiny language models

Evolutionary Feature-wise Thresholding for Binary Representation of NLP Embeddings

A Hybrid Early-Exit Algorithm for Large Language Models Based on Space Alignment Decoding (SPADE)

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