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inverse-scaling/prize
默认分支 main · commit 920f17de · 扫描时间 2026/6/1 01:37:42
星标 621 · Fork 27
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 inverse-scaling/prize 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 archive status
原因:
当前Inverse Scaling Prize **TL;DR: Win up to $100,000 for finding an important task where larger language models do worse.** _~~Submissions due August 27, 2022 (Round 1) and October 27, 2022 (Round 2).~_ The contest has ended! Results: Round 1, Round 2.
复制粘贴的修复Inverse Scaling Prize: Archive of Past Results and Datasets This repository serves as the official archive for the Inverse Scaling Prize, a concluded competition focused on identifying tasks where larger language models perform worse. It provides access to the winning tasks, datasets, and detailed results from Round 1 and Round 2, offering a valuable resource for researchers studying inverse scaling phenomena in LLMs.
- hightopics#2Add relevant topics for categorization
原因:
复制粘贴的修复large-language-models, llm-evaluation, inverse-scaling, ai-safety, machine-learning-datasets, research-archive, benchmark-datasets
- mediumabout#3Update repository description to reflect archive status
原因:
当前A prize for finding tasks that cause large language models to show inverse scaling
复制粘贴的修复Archive of a past prize for finding tasks that cause large language models to show inverse scaling, including results and datasets.
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。
- TextAttack/TextAttack · 被推荐 1 次
- THUDM/OpenAttack · 被推荐 1 次
- marcotcr/lime · 被推荐 1 次
- shap/shap · 被推荐 1 次
- pytorch/captum · 被推荐 1 次
- 品类问题How to identify scenarios where larger language models exhibit unexpected performance degradation?你:未被推荐AI 推荐顺序:
- TextAttack (TextAttack/TextAttack)
- OpenAttack (THUDM/OpenAttack)
- LIME (marcotcr/lime)
- SHAP (shap/shap)
- Captum (pytorch/captum)
AI 推荐了 5 个替代方案,却始终没点名 inverse-scaling/prize。这就是要补上的差距。
查看 AI 完整回答
- 品类问题Where can I find datasets or benchmarks to test limitations of large language models?你:未被推荐AI 推荐顺序:
- Hugging Face Datasets Hub
- EleutherAI's LM Evaluation Harness
- BIG-bench
- MMLU
- HELM
- Adversarial NLI
- TruthfulQA
AI 推荐了 7 个替代方案,却始终没点名 inverse-scaling/prize。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 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 inverse-scaling/prize?passAI 明确点名了 inverse-scaling/prize
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 inverse-scaling/prize in production, what risks or prerequisites should they evaluate first?passAI 明确点名了 inverse-scaling/prize
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 inverse-scaling/prize solve, and who is the primary audience?passAI 未点名 inverse-scaling/prize —— 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 inverse-scaling/prize 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/inverse-scaling/prize)<a href="https://repogeo.com/zh/r/inverse-scaling/prize"><img src="https://repogeo.com/badge/inverse-scaling/prize.svg" alt="RepoGEO" /></a>订阅 Pro,解锁深度诊断
inverse-scaling/prize — 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