Summary of Evaluating the Generalization Ability Of Quantized Llms: Benchmark, Analysis, and Toolbox, by Yijun Liu et al.
Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox
by Yijun Liu, Yuan Meng, Fang Wu, Shenhao Peng, Hang Yao, Chaoyu Guan, Chen Tang, Xinzhu Ma, Zhi Wang, Wenwu Zhu
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 paper investigates the impact of quantization on large language models (LLMs) and their generalization abilities. LLMs have shown promising results in various scenarios, but their high computational demands hinder real-world deployments. Quantization is a technique to reduce memory footprint and inference cost, but it often degrades performance at low bit-widths. The authors provide a comprehensive benchmark suite for evaluating the effects of quantization on LLM generalization, including evaluation systems, detailed analyses, and a general toolbox. They explore how calibration data distribution affects quantized LLM generalization using over 40 datasets across two scenarios, conducting experiments with two well-known LLMs (English and Chinese) and four quantization algorithms. The findings reveal counterintuitive results, such as models quantized with the same calibration set distribution as test data not being necessarily optimal. A modular-designed toolbox is released to facilitate future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how making language models smaller can affect their ability to work well in different situations. Language models are powerful tools that can help computers understand and generate human-like text, but they use a lot of computer memory and processing power. Making them smaller can make them more useful in real-world applications. The authors created a set of tests to evaluate how well language models perform when made smaller, using over 40 different datasets and two types of language models (English and Chinese). They found that the way you prepare the data used to train the model affects its performance when it’s made smaller. |
Keywords
» Artificial intelligence » Generalization » Inference » Quantization
