Summary of Ternaryllm: Ternarized Large Language Model, by Tianqi Chen et al.
TernaryLLM: Ternarized Large Language Model
by Tianqi Chen, Zhe Li, Weixiang Xu, Zeyu Zhu, Dong Li, Lu Tian, Emad Barsoum, Peisong Wang, Jian Cheng
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a novel approach to large language models (LLMs), addressing the issue of high computational costs and memory requirements. The method, called Dual Learnable Ternarization (DLT), enables both scales and shifts in weights to be learnable, mitigating the challenges posed by outliers in both weights and activations. Additionally, the authors introduce Outlier-Friendly Feature Knowledge Distillation (OFF) to recover information lost during extremely low-bit quantization. OFF incorporates semantic information and is insensitive to outliers, leveraging cosine similarity to maximize mutual information between features in ternarized and floating-point models. The proposed approach, TernaryLLM, outperforms previous low-bit quantization methods on standard text generation and zero-shot benchmarks for different LLM families. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making large language models more efficient without sacrificing their performance. Right now, these models are very powerful but use a lot of computer power and memory. The authors came up with a new way to make them work better using something called Dual Learnable Ternarization (DLT). This method helps the model deal with tricky data points that can cause problems. They also developed another technique called Outlier-Friendly Feature Knowledge Distillation (OFF) to help the model remember important information it might forget when it’s made smaller. The new approach, called TernaryLLM, does better than previous methods on tests of language generation and zero-shot learning. |
Keywords
» Artificial intelligence » Cosine similarity » Knowledge distillation » Quantization » Text generation » Zero shot
