zjunlp/Prompt4ReasoningPapers

• highreadme#1

  Reposition the README's opening to clearly state its purpose and unique differentiator

  原因：

  当前

  The current README starts with the H1, then badges, then "## 🔔 News".

  复制粘贴的修复

  复制

  This repository is the official resource for our ACL 2023 survey paper, **'Reasoning with Language Model Prompting: A Survey'**. It provides a comprehensive and continuously updated collection of research papers and resources on prompt engineering for improving LLM reasoning capabilities. **Its core differentiator is that it directly extracts and curates the *exact prompts* used in *published academic papers* investigating *large language model reasoning*, providing a direct resource for researchers rather than just paper lists or general prompt collections.** This curated paper list serves researchers and practitioners in AI/NLP. 
  
  ## 🔔 News

  待办完成忽略
• mediumcomparison#2

  Add a "Comparison" section to the README to differentiate from tools/frameworks

  原因：

  复制粘贴的修复

  复制

  ## 💡 Comparison with LLM Frameworks
  Unlike popular LLM frameworks such as LangChain, LlamaIndex, or Hugging Face Transformers, this repository is not a library or tool for building LLM applications. Instead, it is a research-focused survey and a curated collection of academic papers, specifically highlighting prompt engineering methods for reasoning with language models.

  待办完成忽略
• lowreadme#3

  Add a "Key Features" section to the README to list the repo's offerings

  原因：

  复制粘贴的修复

  复制

  ## ✨ Key Features
  *   **Comprehensive Survey:** A detailed overview of reasoning with language model prompting techniques.
  *   **Curated Paper List:** An up-to-date collection of relevant research papers.
  *   **Extracted Prompts:** Direct access to prompts used in published academic papers.
  *   **Categorized Resources:** Papers organized by reasoning type (e.g., arithmetic, commonsense, logical, symbolic).

  待办完成忽略

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

• 品类问题

  What are effective techniques for improving large language model reasoning capabilities using prompting?

  你：未被推荐
  AI 推荐顺序：

  1. LangChain
  2. LlamaIndex
  3. Weights & Biases Prompts
  4. Humanloop
  5. Hugging Face Transformers
  6. Pinecone
  7. Weaviate
  8. Chroma

  AI 推荐了 8 个替代方案，却始终没点名 zjunlp/Prompt4ReasoningPapers。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Where can I find a comprehensive survey of prompt engineering methods for LLM research?

  你：未被推荐
  AI 推荐顺序：

  1. Prompt Engineering Guide
  2. Awesome Prompt Engineering
  3. Learn Prompting
  4. arXiv
  5. Hugging Face
  6. OpenAI

  AI 推荐了 6 个替代方案，却始终没点名 zjunlp/Prompt4ReasoningPapers。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of zjunlp/Prompt4ReasoningPapers?

  pass
  ⚠AI 未点名 zjunlp/Prompt4ReasoningPapers —— 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 zjunlp/Prompt4ReasoningPapers in production, what risks or prerequisites should they evaluate first?

  pass
  ⚠AI 未点名 zjunlp/Prompt4ReasoningPapers —— 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？

• In one sentence, what problem does the repo zjunlp/Prompt4ReasoningPapers solve, and who is the primary audience?

  pass
  ⚠AI 未点名 zjunlp/Prompt4ReasoningPapers —— 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？