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SinclairCoder/Instruction-Tuning-Papers
默认分支 main · commit 177274f7 · 扫描时间 2026/6/16 07:02:23
星标 769 · Fork 23
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 SinclairCoder/Instruction-Tuning-Papers 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。
- highreadme#1Reposition README opening to clarify it's a paper list
原因:
当前# Instruction-Tuning-Papers A trend starts from `Natrural-Instruction` (ACL 2022), `FLAN` (ICLR 2022) and `T0` (ICLR 2022). What's the instruction-tuning? It aims to teach language models to follow natural language (including prompt, positive or negative examples, and constraints etc.), to perform better multi-task learning on training tasks and generalization on unseen tasks.
复制粘贴的修复# Instruction-Tuning-Papers: A Curated Reading List of Key Papers on Instruction Tuning This repository is a curated reading list of essential research papers on instruction tuning, a pivotal trend in large language model development. It tracks the evolution of instruction tuning from foundational works like Natural-Instruction (ACL 2022), FLAN (ICLR 2022), and T0 (ICLR 2022). Instruction tuning aims to teach language models to follow natural language instructions (including prompts, examples, and constraints) for better multi-task learning and generalization on unseen tasks.
- highlicense#2Add a LICENSE file
原因:
复制粘贴的修复Choose and add a standard open-source LICENSE file (e.g., MIT, Apache-2.0) to the repository root.
- mediumhomepage#3Add a homepage URL to the repository settings
原因:
复制粘贴的修复Add a relevant URL (e.g., a project page, a related blog post, or even the repository URL itself if no external page exists) to the 'Homepage' field in the repository settings.
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。
- LangChain · 被推荐 1 次
- PromptPerfect · 被推荐 1 次
- OpenAI GPT-3/GPT-4 · 被推荐 1 次
- Anthropic Claude · 被推荐 1 次
- Google PaLM 2 / Gemini · 被推荐 1 次
- 品类问题How can I improve large language model performance on unseen tasks using instruction-based methods?你:未被推荐AI 推荐顺序:
- LangChain
- PromptPerfect
- OpenAI GPT-3/GPT-4
- Anthropic Claude
- Google PaLM 2 / Gemini
- Alpaca
- Vicuna
- FLAN
- LlamaIndex
AI 推荐了 9 个替代方案,却始终没点名 SinclairCoder/Instruction-Tuning-Papers。这就是要补上的差距。
查看 AI 完整回答
- 品类问题What are effective strategies for achieving cross-task generalization in natural language processing?你:未被推荐
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 completenesswarn
建议:
- README presencepass
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 SinclairCoder/Instruction-Tuning-Papers?passAI 未点名 SinclairCoder/Instruction-Tuning-Papers —— 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 SinclairCoder/Instruction-Tuning-Papers in production, what risks or prerequisites should they evaluate first?passAI 明确点名了 SinclairCoder/Instruction-Tuning-Papers
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 SinclairCoder/Instruction-Tuning-Papers solve, and who is the primary audience?passAI 未点名 SinclairCoder/Instruction-Tuning-Papers —— 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 SinclairCoder/Instruction-Tuning-Papers 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。
[](https://repogeo.com/zh/r/SinclairCoder/Instruction-Tuning-Papers)<a href="https://repogeo.com/zh/r/SinclairCoder/Instruction-Tuning-Papers"><img src="https://repogeo.com/badge/SinclairCoder/Instruction-Tuning-Papers.svg" alt="RepoGEO" /></a>订阅 Pro,解锁深度诊断
SinclairCoder/Instruction-Tuning-Papers — 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