REPOGEO REPORT · LITE

zjunlp/Prompt4ReasoningPapers

Default branch main · commit bd2e561a · scanned 5/15/2026, 3:02:59 AM

GitHub: 1,004 stars · 67 forks

AI VISIBILITY SCORE

28 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface zjunlp/Prompt4ReasoningPapers, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README opening to clearly state its purpose as a survey and paper list

Why:

CURRENT

# Reasoning with Language Model Prompting Papers

## 🔔 News

COPY-PASTE FIX

# Reasoning with Language Model Prompting Papers
This repository serves as the official companion to our ACL 2023 survey paper, 'Reasoning with Language Model Prompting: A Survey,' providing a comprehensive and curated list of research papers and resources on the topic.

## 🔔 News

mediumhomepage#2
Add the official paper URL as the repository homepage
Why:
COPY-PASTE FIX
```
https://aclanthology.org/2023.acl-long.79/
```

mediumtopics#3

Remove 'datasets' from the repository topics

Why:

CURRENT

arithmetic-reasoning, artificial-intelligence, awsome-list, chain-of-thought, chatgpt, commonsense-reasoning, datasets, gpt-3, language-models, large-language-models, llm, logical-reasoning, natural-language-processing, nlp, paper-list, prompt, prompt-engineering, reasoning, survey, symbolic-reasoning

COPY-PASTE FIX

arithmetic-reasoning, artificial-intelligence, awsome-list, chain-of-thought, chatgpt, commonsense-reasoning, gpt-3, language-models, large-language-models, llm, logical-reasoning, natural-language-processing, nlp, paper-list, prompt, prompt-engineering, reasoning, survey, symbolic-reasoning

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface zjunlp/Prompt4ReasoningPapers

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

GSM8K (Grade School Math 8K)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

GSM8K (Grade School Math 8K) · recommended 1×
AQuA (Abstract Reasoning Corpus) · recommended 1×
MATH dataset · recommended 1×
APPS (Automatic Program Synthesis) · recommended 1×
Flan-T5 · recommended 1×

CATEGORY QUERY
How can I improve large language model capabilities for complex reasoning tasks?
you: not recommended
AI recommended (in order):
1. GSM8K (Grade School Math 8K)
2. AQuA (Abstract Reasoning Corpus)
3. MATH dataset
4. APPS (Automatic Program Synthesis)
5. Flan-T5
6. InstructGPT
7. OpenAI's Code Interpreter
8. ChatGPT Plus
9. LiteLLM (BerriAI/litellm)
10. Google Search API
11. Bing Search API
12. Pinecone
13. Weaviate (weaviate/weaviate)
14. ChromaDB (chroma-core/chroma)
15. Neo4j (neo4j/neo4j)
16. GPT-4
17. Claude 3 Opus
18. Llama 3 70B
19. Mixtral 8x7B
20. GPT-4V
AI recommended 20 alternatives but never named zjunlp/Prompt4ReasoningPapers. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are effective prompting strategies for enhancing language model reasoning abilities?
you: not recommended
AI recommended (in order):
1. Chain-of-Thought (CoT) Prompting
AI recommended 1 alternative but never named zjunlp/Prompt4ReasoningPapers. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of zjunlp/Prompt4ReasoningPapers?
pass
AI named zjunlp/Prompt4ReasoningPapers explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts zjunlp/Prompt4ReasoningPapers in production, what risks or prerequisites should they evaluate first?
pass
AI named zjunlp/Prompt4ReasoningPapers explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo zjunlp/Prompt4ReasoningPapers solve, and who is the primary audience?
pass
AI did not name zjunlp/Prompt4ReasoningPapers — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite