RRepoGEO

REPOGEO REPORT · LITE

RUCAIBox/HaluEval

Default branch main · commit b7253db3 · scanned 6/10/2026, 1:08:07 PM

GitHub: 591 stars · 45 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /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
3 / 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 RUCAIBox/HaluEval, 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

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

OVERALL DIRECTION
  • highreadme#1
    Enhance the README's opening statement for clearer positioning

    Why:

    CURRENT
    # HaluEval: A Hallucination Evaluation Benchmark for LLMs

This is the repo for our paper: HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models. The repo contains:
    COPY-PASTE FIX
    # HaluEval: A Hallucination Evaluation Benchmark for LLMs

This repository provides HaluEval, a comprehensive, large-scale benchmark designed for researchers and developers to rigorously evaluate and understand hallucination in Large Language Models. It includes:
  • mediumhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    Add the URL to the associated paper or a dedicated project page as the repository's homepage.

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 RUCAIBox/HaluEval
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
FEVER
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. FEVER · recommended 2×
  2. TruthfulQA · recommended 2×
  3. HoVer · recommended 1×
  4. FactScore · recommended 1×
  5. FActScore · recommended 1×
  • CATEGORY QUERY
    What are effective methods and benchmarks for evaluating hallucination in large language models?
    you: not recommended
    AI recommended (in order):
    1. FEVER
    2. HoVer
    3. TruthfulQA
    4. FactScore
    5. FActScore
    6. ROUGE
    7. BERTScore
    8. SummEval
    9. Adversarial NLI (ANLI)
    10. Wikidata
    11. DBpedia

    AI recommended 11 alternatives but never named RUCAIBox/HaluEval. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find datasets and tools to assess the factual accuracy of LLM outputs?
    you: not recommended
    AI recommended (in order):
    1. FactBench
    2. FEVER
    3. QAGS
    4. ELI5
    5. TruthfulQA
    6. WikiFact
    7. XSum
    8. LlamaIndex
    9. LangChain
    10. OpenAI Evals
    11. Label Studio
    12. Argilla
    13. Google Fact Check Tools API
    14. NewsGuard API
    15. Sentence-BERT
    16. spaCy

    AI recommended 16 alternatives but never named RUCAIBox/HaluEval. 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 RUCAIBox/HaluEval?
    pass
    AI named RUCAIBox/HaluEval explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts RUCAIBox/HaluEval in production, what risks or prerequisites should they evaluate first?
    pass
    AI named RUCAIBox/HaluEval 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 RUCAIBox/HaluEval solve, and who is the primary audience?
    pass
    AI named RUCAIBox/HaluEval explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)
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RUCAIBox/HaluEval — 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