How Does Quantization Affect Multilingual LLMs?

• URL: http://arxiv.org/abs/2407.03211v1
• Date: Wed, 3 Jul 2024 15:39:40 GMT
• Title: How Does Quantization Affect Multilingual LLMs?
• Authors: Kelly Marchisio, Saurabh Dash, Hongyu Chen, Dennis Aumiller, Ahmet Üstün, Sara Hooker, Sebastian Ruder,
• Abstract summary: Quantization techniques are widely used to improve inference speed and deployment of large language models. We conduct a thorough analysis of quantized multilingual LLMs, focusing on their performance across languages and at varying scales.
• Score: 50.867324914368524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantization techniques are widely used to improve inference speed and deployment of large language models. While a wide body of work examines the impact of quantized LLMs on English tasks, none have examined the effect of quantization across languages. We conduct a thorough analysis of quantized multilingual LLMs, focusing on their performance across languages and at varying scales. We use automatic benchmarks, LLM-as-a-Judge methods, and human evaluation, finding that (1) harmful effects of quantization are apparent in human evaluation, and automatic metrics severely underestimate the detriment: a 1.7% average drop in Japanese across automatic tasks corresponds to a 16.0% drop reported by human evaluators on realistic prompts; (2) languages are disparately affected by quantization, with non-Latin script languages impacted worst; and (3) challenging tasks such as mathematical reasoning degrade fastest. As the ability to serve low-compute models is critical for wide global adoption of NLP technologies, our results urge consideration of multilingual performance as a key evaluation criterion for efficient models.

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