REPOGEO REPORT · LITE
onejune2018/Awesome-LLM-Eval
Default branch main · commit 5b43a7e8 · scanned 6/11/2026, 6:22:42 PM
GitHub: 642 stars · 74 forks
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 onejune2018/Awesome-LLM-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.
- highreadme#1Clarify that this is an 'Awesome List' of resources, not a tool
Why:
CURRENTAwesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on Large Language Models and exploring the boundaries and limits of Generative AI.
COPY-PASTE FIXAwesome-LLM-Eval is a comprehensive *awesome list* of curated resources—including tools, datasets/benchmarks, demos, leaderboards, papers, and models—specifically focused on the Evaluation of Large Language Models and exploring the boundaries of Generative AI.
- highreadme#2Add a clear statement about the project's current relevance and active maintenance
Why:
COPY-PASTE FIXThis project is actively maintained and regularly updated to reflect the latest advancements in LLM evaluation, serving as the live companion to our survey paper.
- mediumtopics#3Add more specific 'awesome list' related topics
Why:
CURRENTawsome-list, awsome-lists, benchmark, bert, chatglm, chatgpt, dataset, evaluation, gpt3, large-language-model, leaderboard, llama, llm, llm-evaluation, machine-learning, nlp, openai, qwen, rag
COPY-PASTE FIXawsome-list, awsome-lists, benchmark, bert, chatglm, chatgpt, dataset, evaluation, gpt3, large-language-model, leaderboard, llama, llm, llm-evaluation, machine-learning, nlp, openai, qwen, rag, awesome-llm-evaluation, awesome-generative-ai, llm-resources, ai-evaluation-list
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.
- Hugging Face Open LLM Leaderboard · recommended 1×
- EleutherAI's LM Evaluation Harness · recommended 1×
- OpenAI Evals · recommended 1×
- AlpacaEval · recommended 1×
- MT-Bench · recommended 1×
- CATEGORY QUERYWhat are the best resources for evaluating large language models' performance and capabilities?you: not recommendedAI recommended (in order):
- Hugging Face Open LLM Leaderboard
- EleutherAI's LM Evaluation Harness
- OpenAI Evals
- AlpacaEval
- MT-Bench
- MMLU
- TruthfulQA
- HELM
- BIG-bench
- Argilla
- LangChain
- LlamaIndex
- Weights & Biases
- DeepEval
AI recommended 14 alternatives but never named onejune2018/Awesome-LLM-Eval. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhere can I find a comprehensive list of benchmarks and datasets for assessing generative AI models?you: not recommendedAI recommended (in order):
- Hugging Face Datasets and Benchmarks
- Papers With Code
- EleutherAI's LM Evaluation Harness (EleutherAI/lm-evaluation-harness)
- OpenAI Evals (openai/evals)
- Google's BIG-bench (google/BIG-bench)
- Kaggle Datasets
- Awesome Generative AI List
AI recommended 7 alternatives but never named onejune2018/Awesome-LLM-Eval. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesspass
- README presencepass
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 onejune2018/Awesome-LLM-Eval?passAI named onejune2018/Awesome-LLM-Eval explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts onejune2018/Awesome-LLM-Eval in production, what risks or prerequisites should they evaluate first?passAI named onejune2018/Awesome-LLM-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 onejune2018/Awesome-LLM-Eval solve, and who is the primary audience?passAI named onejune2018/Awesome-LLM-Eval explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
Drop this badge into the README of onejune2018/Awesome-LLM-Eval. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
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onejune2018/Awesome-LLM-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