RRepoGEO

REPOGEO REPORT · LITE

princeton-nlp/LESS

Default branch main · commit 8abf9628 · scanned 6/13/2026, 11:27:56 AM

GitHub: 528 stars · 47 forks

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 princeton-nlp/LESS, 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 repo's purpose as a data selection tool for LLM instruction tuning in the README's opening

    Why:

    CURRENT
    This repo contains the code for our ICML 2024 paper LESS: Selecting Influential Data for Targeted Instruction Tuning. In this work, we propose a data selection method to select influential data to induce a target capability.
    COPY-PASTE FIX
    LESS provides a practical, code-based method for **selecting influential training data to enhance targeted instruction tuning of large language models (LLMs)**. This repository contains the official implementation for our ICML 2024 paper, 'LESS: Selecting Influential Data for Targeted Instruction Tuning,' which proposes this novel data selection approach to induce specific target capabilities.
  • mediumtopics#2
    Add more specific topics related to LLM fine-tuning and data optimization

    Why:

    CURRENT
    data, data-selection, influence, instruction-tuning, llama, llm, mistral
    COPY-PASTE FIX
    data, data-selection, influence, instruction-tuning, llama, llm, mistral, llm-fine-tuning, data-optimization, model-tuning
  • lowhomepage#3
    Add the paper's arXiv link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2402.06020

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 princeton-nlp/LESS
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
argilla-io/argilla
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. argilla-io/argilla · recommended 1×
  2. heartexlabs/label-studio · recommended 1×
  3. snorkel-team/snorkel · recommended 1×
  4. cleanlab/cleanlab · recommended 1×
  5. OpenAI's API · recommended 1×
  • CATEGORY QUERY
    How can I select the most effective training data for targeted LLM instruction tuning?
    you: not recommended
    AI recommended (in order):
    1. Argilla (argilla-io/argilla)
    2. Label Studio (heartexlabs/label-studio)
    3. Snorkel (snorkel-team/snorkel)
    4. Cleanlab (cleanlab/cleanlab)
    5. OpenAI's API
    6. Claude
    7. Gemini
    8. Cohere's Embeddings
    9. OpenAI's `text-embedding-ada-002`
    10. GPT-4
    11. Claude 3
    12. Llama 3
    13. NLPAug (makcedward/nlpaug)
    14. TextAttack (textattack/textattack)
    15. Alpaca (tatsu-lab/stanford_alpaca)
    16. Dolly 2.0 (databrickslabs/dolly)
    17. ShareGPT
    18. FLAN

    AI recommended 18 alternatives but never named princeton-nlp/LESS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What methods exist for identifying influential data points to enhance large language model fine-tuning?
    you: not recommended
    AI recommended (in order):
    1. PyTorch-Influence-Functions
    2. Shapley
    3. PyTorch
    4. TensorFlow
    5. Hugging Face Transformers
    6. modAL
    7. UMAP
    8. t-SNE
    9. PCA

    AI recommended 9 alternatives but never named princeton-nlp/LESS. 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 princeton-nlp/LESS?
    pass
    AI named princeton-nlp/LESS explicitly

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

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

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

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princeton-nlp/LESS — 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