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REPOGEO REPORT · LITE

AIoT-MLSys-Lab/Efficient-LLMs-Survey

Default branch main · commit ef0d8ae6 · scanned 5/22/2026, 6:39:04 PM

GitHub: 1,257 stars · 98 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
22 /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
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 AIoT-MLSys-Lab/Efficient-LLMs-Survey, 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 opening to clarify it's a research survey repo

    Why:

    CURRENT
    # Efficient Large Language Models: A Survey  

> **Efficient Large Language Models: A Survey**[ [arXiv]](http://arxiv.org/abs/2312.03863) (Version 1: 12/06/2023; Version 2: 12/23/2023; Version 3: 01/31/2024; Version 4: 05/23/2024, camera ready version of Transactions on Machine Learning Research)
    COPY-PASTE FIX
    # Efficient Large Language Models: A Survey  

> **This repository serves as the official companion resource for our TMLR 2024 paper, providing a curated collection of resources, code links, and datasets discussed in the survey.**
> **Efficient Large Language Models: A Survey**[ [arXiv]](http://arxiv.org/abs/2312.03863) (Version 1: 12/06/2023; Version 2: 12/23/2023; Version 3: 01/31/2024; Version 4: 05/23/2024, camera ready version of Transactions on Machine Learning Research)
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the chosen license text (e.g., MIT, Apache-2.0, or a custom one if applicable to the survey content).
  • mediumtopics#3
    Add more specific topics to clarify the repo's research nature

    Why:

    CURRENT
    efficient-deep-learning, generative-ai, large-language-models, machine-learning-systems, survey
    COPY-PASTE FIX
    efficient-deep-learning, generative-ai, large-language-models, machine-learning-systems, survey, llm-efficiency, research-resources, academic-survey

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 AIoT-MLSys-Lab/Efficient-LLMs-Survey
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch Quantization
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Quantization · recommended 1×
  2. ONNX Runtime · recommended 1×
  3. TensorFlow Lite · recommended 1×
  4. NVIDIA TensorRT · recommended 1×
  5. PyTorch Pruning · recommended 1×
  • CATEGORY QUERY
    What are the latest techniques for optimizing large language models for resource-constrained environments?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Quantization
    2. ONNX Runtime
    3. TensorFlow Lite
    4. NVIDIA TensorRT
    5. PyTorch Pruning
    6. TensorFlow Model Optimization Toolkit
    7. OpenVINO Toolkit
    8. Hugging Face Transformers
    9. PaddlePaddle PaddleSlim
    10. LoRA (Low-Rank Adaptation)
    11. peft library from Hugging Face
    12. QLoRA
    13. Prefix-Tuning
    14. P-Tuning v2
    15. TinyLlama
    16. Phi-2
    17. NVIDIA TensorRT-LLM
    18. Google's MedLM

    AI recommended 18 alternatives but never named AIoT-MLSys-Lab/Efficient-LLMs-Survey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive overview of methods to reduce LLM computational cost?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes (TimDettmers/bitsandbytes)
    2. FlashAttention (Dao-AILab/flash-attention)
    3. PEFT (huggingface/peft)
    4. transformers (huggingface/transformers)
    5. optimum (huggingface/optimum)
    6. TensorRT-LLM (NVIDIA/TensorRT-LLM)
    7. DeepSpeed (microsoft/DeepSpeed)

    AI recommended 7 alternatives but never named AIoT-MLSys-Lab/Efficient-LLMs-Survey. 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 AIoT-MLSys-Lab/Efficient-LLMs-Survey?
    pass
    AI did not name AIoT-MLSys-Lab/Efficient-LLMs-Survey — 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 AIoT-MLSys-Lab/Efficient-LLMs-Survey in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AIoT-MLSys-Lab/Efficient-LLMs-Survey 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 AIoT-MLSys-Lab/Efficient-LLMs-Survey solve, and who is the primary audience?
    pass
    AI did not name AIoT-MLSys-Lab/Efficient-LLMs-Survey — 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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AIoT-MLSys-Lab/Efficient-LLMs-Survey — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite