---
title: "ml-surveys vs awesome-llm-human-preference-datasets"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/eugeneyan-ml-surveys-vs-glgh-awesome-llm-human-preference-datasets"
tools: ["eugeneyan-ml-surveys", "glgh-awesome-llm-human-preference-datasets"]
---

# ml-surveys vs awesome-llm-human-preference-datasets

*GraphCanon updated Aug 22, 2026*

## 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.

[ml-surveys](https://github.com/eugeneyan/ml-surveys) reports 2.9k GitHub stars, 292 forks, and 2 open issues, last pushed Mar 17, 2023. [awesome-llm-human-preference-datasets](https://github.com/glgh/awesome-llm-human-preference-datasets) has 390 stars, 19 forks, and 0 open issues, last pushed Oct 4, 2023. Figures are from public GitHub metadata via [ml-surveys's repository](https://github.com/eugeneyan/ml-surveys) and [awesome-llm-human-preference-datasets's repository](https://github.com/glgh/awesome-llm-human-preference-datasets).

| | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [awesome-llm-human-preference-datasets](/tools/glgh-awesome-llm-human-preference-datasets.md) |
| --- | --- | --- |
| Tagline | Survey papers summarizing advances in various AI domains | Curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval |
| Stars | 2,902 | 390 |
| Forks | 292 | 19 |
| Open issues | 2 | 0 |
| Language | - | - |
| Adopt for | 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 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 | - | - |
| Runtime | - | - |
| License | MIT | MIT |
| Categories | Computer Vision, Evaluation & Observability, Model Training | Evaluation & Observability, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [awesome-llm-human-preference-datasets](/tools/glgh-awesome-llm-human-preference-datasets.md) |
| --- | --- | --- |
| Days since push | 1254d | 1036d |
| Open issues (now) | 2 | 0 |
| Stars delta | 0 (30d) | Unknown |
| Open issues delta | 0 (30d) | Unknown |
| Full report | [trust report](/tools/eugeneyan-ml-surveys/trust.md) | [trust report](/tools/glgh-awesome-llm-human-preference-datasets/trust.md) |

## Decision facts: ml-surveys

- **Adopt for:** 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.

## Decision facts: awesome-llm-human-preference-datasets

- **Adopt for:** 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

## Choose when

### 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

### 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 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 NOT to use awesome-llm-human-preference-datasets

- NLP，LLM、，。

## 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](/tools/eugeneyan-ml-surveys/alternatives) and [awesome-llm-human-preference-datasets alternatives](/tools/glgh-awesome-llm-human-preference-datasets/alternatives) ([ml-surveys markdown twin](/tools/eugeneyan-ml-surveys/alternatives.md), [awesome-llm-human-preference-datasets markdown twin](/tools/glgh-awesome-llm-human-preference-datasets/alternatives.md)), 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](/compare/eugeneyan-ml-surveys-vs-glgh-awesome-llm-human-preference-datasets.md) 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](/tools/eugeneyan-ml-surveys/trust); [awesome-llm-human-preference-datasets trust report](/tools/glgh-awesome-llm-human-preference-datasets/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=eugeneyan-ml-surveys`](/api/graphcanon/graph?tool=eugeneyan-ml-surveys)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_