RRepoGEO

REPOGEO REPORT · LITE

EdinburghNLP/awesome-hallucination-detection

Default branch main · commit 64920cc6 · scanned 5/9/2026, 5:08:02 PM

GitHub: 1,083 stars · 88 forks

AI VISIBILITY SCORE
15 /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
0 / 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 EdinburghNLP/awesome-hallucination-detection, 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
    Clarify the README's opening to state its purpose as an 'awesome list' of papers

    Why:

    CURRENT
    # awesome-hallucination-detection

## Papers and Summaries
    COPY-PASTE FIX
    # awesome-hallucination-detection

This repository is an **awesome list** of research papers, tools, and datasets focused on **hallucination detection in Large Language Models (LLMs)**. It serves as a curated resource for NLP researchers and practitioners to explore the latest advancements and methodologies in evaluating and mitigating factual inaccuracies in generative AI.

## Papers and Summaries
  • mediumtopics#2
    Expand repository topics to include 'awesome-list' and 'research-survey' keywords

    Why:

    CURRENT
    hallucinations, llms, nlp
    COPY-PASTE FIX
    hallucinations, llms, nlp, awesome-list, research-survey, evaluation, trustworthy-ai, ai-safety
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/EdinburghNLP/awesome-hallucination-detection

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 EdinburghNLP/awesome-hallucination-detection
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Scale AI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Scale AI · recommended 2×
  2. Wikidata · recommended 2×
  3. Ragas · recommended 1×
  4. TruLens · recommended 1×
  5. DeepEval · recommended 1×
  • CATEGORY QUERY
    How can I effectively detect and measure factual inaccuracies in large language model responses?
    you: not recommended
    AI recommended (in order):
    1. Ragas
    2. TruLens
    3. DeepEval
    4. Label Studio
    5. Prodigy
    6. Scale AI
    7. LangChain
    8. Neo4j
    9. DBPedia
    10. Wikidata
    11. GPT-4
    12. Claude 3 Opus

    AI recommended 12 alternatives but never named EdinburghNLP/awesome-hallucination-detection. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What techniques are available for evaluating the trustworthiness and reducing hallucinations in generative AI models?
    you: not recommended
    AI recommended (in order):
    1. Scale AI
    2. Appen
    3. Wikidata
    4. Google Knowledge Graph API
    5. Neo4j (neo4j/neo4j)
    6. Vaticle's TypeDB (vaticle/typedb)
    7. LangChain (langchain-ai/langchain)
    8. LlamaIndex (run-llama/llama_index)
    9. Faiss (facebookresearch/faiss)
    10. Pinecone
    11. Weaviate (weaviate/weaviate)
    12. ChromaDB (chroma-core/chroma)
    13. PyTorch (pytorch/pytorch)
    14. TensorFlow (tensorflow/tensorflow)
    15. OpenAI Moderation API
    16. NeMo Guardrails (NVIDIA/NeMo-Guardrails)
    17. Hugging Face Transformers (huggingface/transformers)
    18. LIME (marcotcr/lime)
    19. SHAP (shap/shap)

    AI recommended 19 alternatives but never named EdinburghNLP/awesome-hallucination-detection. 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 EdinburghNLP/awesome-hallucination-detection?
    pass
    AI did not name EdinburghNLP/awesome-hallucination-detection — 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?

  • If a team adopts EdinburghNLP/awesome-hallucination-detection in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name EdinburghNLP/awesome-hallucination-detection — 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?

  • In one sentence, what problem does the repo EdinburghNLP/awesome-hallucination-detection solve, and who is the primary audience?
    pass
    AI did not name EdinburghNLP/awesome-hallucination-detection — 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?

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EdinburghNLP/awesome-hallucination-detection — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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