Comparison
ml-surveys vs awesome-llm-human-preference-datasets
Verdict
Pick ml-surveys if ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems; pick awesome-llm-human-preference-datasets if awesome-llm-human-preference-datasets is an open-source repository that curates a collection of human preference datasets for fine-tuning large language models (LLMs), with a focus on reinforcement learning with human反馈被.
Markdown twin · ml-surveys alternatives · awesome-llm-human-preference-datasets alternatives
GraphCanon updated today
vs
awesome-llm-human-preference-datasets
glgh/awesome-llm-human-preference-datasets
★ 390pushed Oct 4, 2023
Trust & integrity
| Signal | ml-surveys | awesome-llm-human-preference-datasets |
|---|---|---|
| Maintenance | Dormant (1254d since push) As of today · github_public_v1 | Dormant (1036d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- ml-surveys
- Survey papers summarizing advances in various AI domains
- awesome-llm-human-preference-datasets
- Curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval
Stars
- ml-surveys
- 2.9k
- awesome-llm-human-preference-datasets
- 390
Forks
- ml-surveys
- 292
- awesome-llm-human-preference-datasets
- 19
Open issues
- ml-surveys
- 2
- awesome-llm-human-preference-datasets
- 0
Language
- ml-surveys
- -
- awesome-llm-human-preference-datasets
- -
Adopt for
- ml-surveys
- ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems.
- awesome-llm-human-preference-datasets
- awesome-llm-human-preference-datasets is an open-source repository that curates a collection of human preference datasets for fine-tuning large language models (LLMs), with a focus on reinforcement learning with human反馈被
Persona
- ml-surveys
- -
- awesome-llm-human-preference-datasets
- -
Runtime
- ml-surveys
- -
- awesome-llm-human-preference-datasets
- -
License
- ml-surveys
- MIT
- awesome-llm-human-preference-datasets
- MIT
Last pushed
- ml-surveys
- Mar 17, 2023
- awesome-llm-human-preference-datasets
- Oct 4, 2023
Categories
- ml-surveys
- Computer Vision, Evaluation & Observability, Model Training
- awesome-llm-human-preference-datasets
- Evaluation & Observability, Model Training
Trust and health
Days since push
- ml-surveys
- 1254d
- awesome-llm-human-preference-datasets
- 1036d
Open issues (now)
- ml-surveys
- 2
- awesome-llm-human-preference-datasets
- 0
Stars delta
- ml-surveys
- 0 (30d)
- awesome-llm-human-preference-datasets
- Unknown
Open issues delta
- ml-surveys
- 0 (30d)
- awesome-llm-human-preference-datasets
- Unknown
Full report
- ml-surveys
- Trust report
- awesome-llm-human-preference-datasets
- Trust report
Choose ml-surveys if…
- Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, recommender-system.
- Also covers Computer Vision.
- When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning
When NOT to use ml-surveys
- If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary
- In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
Choose awesome-llm-human-preference-datasets if…
- Tags unique to awesome-llm-human-preference-datasets: awesome-list, datasets, eval, human-preferences.
- 当你需要对大型语言模型（LLM）进行微调，并希望使用经过人类评估的数据集来增强模型性能，尤其是在强化学习场景中时。
- More recently updated (last pushed Oct 4, 2023).
When NOT to use awesome-llm-human-preference-datasets
- NLP，LLM、，。
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (eugeneyan/ml-surveys) · observed Aug 22, 2026
- GitHub forks (eugeneyan/ml-surveys) · observed Aug 22, 2026
- Last push (eugeneyan/ml-surveys) · observed Mar 17, 2023
- License file (MIT) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (glgh/awesome-llm-human-preference-datasets) · observed Aug 6, 2026
- GitHub forks (glgh/awesome-llm-human-preference-datasets) · observed Aug 6, 2026
- Last push (glgh/awesome-llm-human-preference-datasets) · observed Oct 4, 2023
- License file (MIT) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ml-surveys 2.9k · awesome-llm-human-preference-datasets 390 (synced Aug 22, 2026).
Common questions
- What is the difference between ml-surveys and awesome-llm-human-preference-datasets?
- ml-surveys: Survey papers summarizing advances in various AI domains. awesome-llm-human-preference-datasets: Curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval. See the comparison table for live GitHub stats and shared categories.
- When should I choose ml-surveys over awesome-llm-human-preference-datasets?
- Choose ml-surveys over awesome-llm-human-preference-datasets when Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, recommender-system; Also covers Computer Vision; When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning.
- When should I choose awesome-llm-human-preference-datasets over ml-surveys?
- Choose awesome-llm-human-preference-datasets over ml-surveys when Tags unique to awesome-llm-human-preference-datasets: awesome-list, datasets, eval, human-preferences; 当你需要对大型语言模型（LLM）进行微调，并希望使用经过人类评估的数据集来增强模型性能，尤其是在强化学习场景中时。; More recently updated (last pushed Oct 4, 2023).
- When should I avoid ml-surveys?
- If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
- When should I avoid awesome-llm-human-preference-datasets?
- NLP，LLM、，。
- Is ml-surveys or awesome-llm-human-preference-datasets more popular on GitHub?
- ml-surveys has more GitHub stars (2,902 vs 390). Stars measure visibility, not whether either tool fits your constraints.
- Are ml-surveys and awesome-llm-human-preference-datasets open source?
- Yes - both are open-source projects on GitHub (ml-surveys: MIT, awesome-llm-human-preference-datasets: MIT).
- Where can I find alternatives to ml-surveys or awesome-llm-human-preference-datasets?
- GraphCanon lists graph-backed alternatives at ml-surveys alternatives and awesome-llm-human-preference-datasets alternatives (ml-surveys markdown twin, awesome-llm-human-preference-datasets markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, ml-surveys or awesome-llm-human-preference-datasets?
- ml-surveys: Dormant. awesome-llm-human-preference-datasets: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for ml-surveys and awesome-llm-human-preference-datasets?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ml-surveys trust report; awesome-llm-human-preference-datasets trust report.