图解检索增强Transformer：RETRO如何以4%参数媲美GPT-3

AI 摘要

DeepMind 的 RETRO 模型通过检索数据库增强语言模型，以 7.5B 参数达到 GPT-3 的性能，仅为后者规模的 4%。该模型将事实知识存储在外部数据库中，减少参数负担，训练更快且部署成本更低。文章强调构建更大模型并非提升性能的唯一途径，检索增强是重要方向。

正文节选

The Illustrated Retrieval Transformer Discussion: Discussion Thread for comments, corrections, or any feedback. Translations: Korean, Russian Summary: The latest batch of language models can be much smaller yet achieve GPT-3 like performance by being able to query a database or search the web for information. A key indication is that building larger and larger models is not the only way to improve performance. Video The last few years saw the rise of Large Language Models (LLMs) – machine lear

发布时间：2022-01-03 08:00

抓取时间：2026-08-02 00:26

来源机构：Jay Alammar