Summary of The Impact Of Quantization on Retrieval-augmented Generation: An Analysis Of Small Llms, by Mert Yazan et al.
The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs
by Mert Yazan, Suzan Verberne, Frederik Situmeang
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 how post-training quantization affects the abilities of smaller Large Language Models (LLMs) in performing retrieval-augmented generation (RAG), specifically in longer contexts. The authors chose personalization as the evaluation domain, which requires long-context reasoning over multiple documents. They compared the original FP16 and INT4-quantized performance of 7B and 8B LLMs on two tasks while increasing the number of retrieved documents to test their performance against longer contexts. The findings reveal that if a 7B LLM performs well, quantization does not impair its performance and long-context reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how smaller Language Models can still be useful after being reduced in size by using post-training quantization. They tested these smaller models to see if they can still do tasks like generating text based on what they found in multiple documents. The results showed that even with reduced sizes, some of the language models were still able to perform well. |
Keywords
» Artificial intelligence » Quantization » Rag » Retrieval augmented generation
