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RenzeLou/awesome-instruction-learning

默认分支 main · commit c0ac59b4 · 扫描时间 2026/6/12 05:27:39

星标 510 · Fork 23

AI 可见性总分
27 /100
亟需修复
品类召回
0 / 2
在所有问题中均未被推荐
规则结果
通过 2 · 警告 0 · 失败 0
客观元数据检查
AI 认识你的名字
1 / 3
直接询问时，AI 是否点名你的仓库
如何阅读这份报告

行动计划告诉你下一步要做什么——按影响力排序、可直接复制粘贴的修改。品类可见性是真正的 GEO 测试：当用户向 AI 提一个不带品牌、本应让 RenzeLou/awesome-instruction-learning 浮出水面的问题时，AI 是真的推荐了你，还是推荐了你的竞品？客观检查验证 AI 引擎最先权衡的那些元数据信号。自指检查判断 AI 是否还认识你的名字。

行动计划 — 可复制粘贴的修复

3 条由 gemini-2.5-flash 生成、按优先级排序的修改。修完后请把对应条目标记为完成。

整体方向
  • highreadme#1
    Reposition README opening to explicitly state its 'awesome list' nature and scope

    原因：

    当前
    <h1 align="center"> Awesome Instruction Learning </h1>

<p align="center">
  <a href="https://github.com/RenzeLou/awesome-instruction-learning"></a>
  <a href="https://github.com/RenzeLou/awesome-instruction-learning#-star-history"></a>
</p>

<p align="center">
  <a href="https://github.com/RenzeLou/awesome-instruction-learning/commits/main"></a>
  <a href="https://github.com/RenzeLou/awesome-instruction-learning/blob/main/count_number.py"></a>
  <a href="https://github.com/RenzeLou/awesome-instruction-learning/pulls"></a>
</p>

<p align="center">
🔥🔥🔥 An awesome reading list of <b>Instruction Tuning and Following</b>, including <em>papers</em> and <em>datasets</em>. 
</p>
    复制粘贴的修复
    <h1 align="center"> Awesome Instruction Learning </h1>

<p align="center">
  <a href="https://github.com/RenzeLou/awesome-instruction-learning"></a>
  <a href="https://github.com/RenzeLou/awesome-instruction-learning#-star-history"></a>
</p>

<p align="center">
  <a href="https://github.com/RenzeLou/awesome-instruction-learning/commits/main"></a>
  <a href="https://github.com/RenzeLou/awesome-instruction-learning/blob/main/count_number.py"></a>
  <a href="https://github.com/RenzeLou/awesome-instruction-learning/pulls"></a>
</p>

<p align="center">
🔥🔥🔥 The most comprehensive and up-to-date awesome list for <b>Instruction Tuning and Following</b>, featuring curated <em>papers</em> and <em>datasets</em>. 
</p>
  • mediumabout#2
    Update repository description to mention the associated survey

    原因：

    当前
    Papers and Datasets on Instruction Tuning and Following. ✨✨✨
    复制粘贴的修复
    A comprehensive awesome list of Papers and Datasets on Instruction Tuning and Following, directly associated with our latest survey: https://arxiv.org/abs/2303.10475
  • lowtopics#3
    Add 'instruction-following' to repository topics

    原因：

    当前
    awesome-list, datasets, in-context-learning, instruction, instruction-learning, instruction-tuning, large-language-models, paper-list, pretrained-language-model, prompt, survey
    复制粘贴的修复
    awesome-list, datasets, in-context-learning, instruction, instruction-learning, instruction-tuning, instruction-following, large-language-models, paper-list, pretrained-language-model, prompt, survey

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

品类可见性 — 真正的 GEO 测试

向 google/gemini-2.5-flash 提出的不带品牌问题。AI 推荐了你，还是推荐了别人？

各模型使用同一组问题 — 切换标签对比回答与排名。

召回
0 / 2
0% 的问题里出现了 RenzeLou/awesome-instruction-learning
平均排名
—
越小越好。#1 表示首位推荐。
声量占比
0%
在所有被点名的工具中，你占了多少？
头号对手
Awesome-Instruction-Tuning
在 2 个问题中被推荐 1 次
竞品排行
  1. Awesome-Instruction-Tuning · 被推荐 1 次
  2. Papers With Code · 被推荐 1 次
  3. arXiv · 被推荐 1 次
  4. Hugging Face · 被推荐 1 次
  5. Google Scholar · 被推荐 1 次
  • 品类问题
    Where can I find a comprehensive list of papers on instruction tuning for large language models?
    你：未被推荐
    AI 推荐顺序：
    1. Awesome-Instruction-Tuning
    2. Papers With Code
    3. arXiv
    4. Hugging Face
    5. Google Scholar
    6. Semantic Scholar
    7. r/MachineLearning
    8. r/DeepLearning

    AI 推荐了 8 个替代方案，却始终没点名 RenzeLou/awesome-instruction-learning。这就是要补上的差距。

    查看 AI 完整回答
  • 品类问题
    What are the key datasets and latest research surveys for instruction following in LLMs?
    你：未被推荐
    AI 推荐顺序：
    1. Alpaca
    2. Dolly 2.0
    3. FLAN
    4. ShareGPT
    5. P3
    6. Self-Instruct
    7. OpenAssistant Conversations Dataset (OASST1)
    8. A Survey of Large Language Models (2023) by Zhao et al.
    9. Instruction Tuning for Large Language Models: A Survey (2023) by Wang et al.
    10. Harnessing the Power of LLMs: A Survey on ChatGPT-like Models (2023) by Chang et al.
    11. A Comprehensive Survey of AI-Generated Content (AIGC): From Text to Image and Beyond (2023) by Cao et al.
    12. Alignment of Large Language Models: A Survey (2023) by Ji et al.

    AI 推荐了 12 个替代方案，却始终没点名 RenzeLou/awesome-instruction-learning。这就是要补上的差距。

    查看 AI 完整回答

客观检查

针对 AI 引擎最看重的元数据信号的规则审计。

  • Metadata completeness
    pass

  • README presence
    pass

自指检查

当被直接问到你时，AI 是否还知道你的仓库存在？

  • Compared to common alternatives in this category, what is the core differentiator of RenzeLou/awesome-instruction-learning?
    pass
    AI 未点名 RenzeLou/awesome-instruction-learning —— 很可能在说另一个项目

    AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

  • If a team adopts RenzeLou/awesome-instruction-learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI 明确点名了 RenzeLou/awesome-instruction-learning

    AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

  • In one sentence, what problem does the repo RenzeLou/awesome-instruction-learning solve, and who is the primary audience?
    pass
    AI 未点名 RenzeLou/awesome-instruction-learning —— 很可能在说另一个项目

    AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

嵌入你的 GEO 徽章

把这个徽章贴进 RenzeLou/awesome-instruction-learning 的 README。每次重新扫描都会自动更新，并跳到最新报告——是「我在乎 AI 可发现性」最简单的公开证明。

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订阅 Pro，解锁深度诊断

RenzeLou/awesome-instruction-learning — 轻量扫描仍免费；本卡列出 Pro 相对轻量的深度额度。

  • 深度报告每月 10 次
  • 无品牌品类查询5，轻量 2
  • 优先行动项8，轻量 3