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English(EN) Why my from-scratch Japanese LLM says "That sounds tough" to good news

开发者定制的日语 LLM 因数据不平衡而难以处理积极情绪

一位开发者从头开始构建了一个名为 Lilas 的日语对话 AI 模型,该模型表现出一种奇怪的倾向,即对积极消息回应“听起来很难”。对原始训练数据的初步分析表明,该短语的出现频率过高。然而,对数据进行更细致的加权,考虑到训练过程中特定文件的重复次数,发现“我很高兴听到这个”这个短语实际上要少得多,其出现频率明显低于“听起来很难”。 AI

影响 强调了平衡训练数据对于细致的对话式 AI 的关键重要性。

排序理由 开发者博客文章,详细介绍了构建和调试自定义 LLM 的过程。

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

开发者定制的日语 LLM 因数据不平衡而难以处理积极情绪

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开发者博客文章,详细介绍了构建和调试自定义 LLM 的过程。
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报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Kate ·

    为什么我从零开始构建的日语大语言模型对好消息说“这听起来很难”

    <h2> TL;DR </h2> <p>Lilas is a ~40M-parameter Japanese chat model I built from scratch, tokenizer included. It kept answering cheerful remarks with "That sounds tough, please don't overdo it." A raw count of the training data pointed at one over-used phrase. Weighting the counts …