RRepoGEO

REPOGEO REPORT · LITE

tatsu-lab/alpaca_eval

Default branch main · commit cd543a14 · scanned 5/11/2026, 9:57:21 AM

GitHub: 1,986 stars · 308 forks

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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 tatsu-lab/alpaca_eval, 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 README's opening sentence to clarify repo's role as a tool

    Why:

    CURRENT
    AlpacaEval : An Automatic Evaluator for Instruction-following Language Models
    COPY-PASTE FIX
    # AlpacaEval: The Official Implementation and Tools for Automatic LLM Evaluation

This repository provides the official implementation and tools for AlpacaEval, an automatic evaluator for instruction-following language models. Our goal is to offer a benchmark for chat LLMs that is fast (< 5min), cheap (< $10), and highly correlated with humans (0.98).
  • mediumreadme#2
    Add a concise 'Why use this repo?' statement early in the README

    Why:

    COPY-PASTE FIX
    ## Why AlpacaEval (the tool)?

AlpacaEval addresses the critical need for a programmatic, cost-effective, and highly human-correlated method to evaluate instruction-following LLMs. Unlike manual evaluations, this repository provides the framework to run evaluations quickly and affordably, leveraging powerful LLMs as automatic judges.
  • lowtopics#3
    Add 'benchmark' to the repository topics

    Why:

    CURRENT
    deep-learning, evaluation, foundation-models, instruction-following, large-language-models, leaderboard, nlp, rlhf
    COPY-PASTE FIX
    benchmark, deep-learning, evaluation, foundation-models, instruction-following, large-language-models, leaderboard, nlp, rlhf

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 tatsu-lab/alpaca_eval
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Evals
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Evals · recommended 1×
  2. EleutherAI/lm-evaluation-harness · recommended 1×
  3. Ragas · recommended 1×
  4. Humanloop · recommended 1×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    How to automatically evaluate instruction-following large language models quickly and affordably?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Evals
    2. LM-Harness (EleutherAI/lm-evaluation-harness)
    3. Ragas
    4. Humanloop
    5. LangChain
    6. Weights & Biases
    7. pytest
    8. unittest

    AI recommended 8 alternatives but never named tatsu-lab/alpaca_eval. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are reliable, cost-effective benchmarks for assessing LLM response quality against human judgment?
    you: not recommended
    AI recommended (in order):
    1. MT-Bench
    2. AlpacaEval 2.0
    3. Chatbot Arena
    4. HELM
    5. OpenAssistant Conversations Dataset (OASST1)
    6. Argilla
    7. Label Studio

    AI recommended 7 alternatives but never named tatsu-lab/alpaca_eval. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 tatsu-lab/alpaca_eval?
    pass
    AI did not name tatsu-lab/alpaca_eval — 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 tatsu-lab/alpaca_eval in production, what risks or prerequisites should they evaluate first?
    pass
    AI named tatsu-lab/alpaca_eval 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 tatsu-lab/alpaca_eval solve, and who is the primary audience?
    pass
    AI did not name tatsu-lab/alpaca_eval — 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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tatsu-lab/alpaca_eval — 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