---
title: "Awesome-LLM-Eval vs Awesome-LLM-in-Social-Science"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/onejune2018-awesome-llm-eval-vs-valuebyte-ai-awesome-llm-in-social-science"
tools: ["onejune2018-awesome-llm-eval", "valuebyte-ai-awesome-llm-in-social-science"]
---

# Awesome-LLM-Eval vs Awesome-LLM-in-Social-Science

*GraphCanon updated Jul 28, 2026*

## Verdict

Pick Awesome-LLM-Eval if awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks; pick Awesome-LLM-in-Social-Science if curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more.

[Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) reports 654 GitHub stars, 82 forks, and 44 open issues, last pushed Nov 24, 2025. [Awesome-LLM-in-Social-Science](https://github.com/ValueByte-AI/Awesome-LLM-in-Social-Science) has 639 stars, 48 forks, and 0 open issues, last pushed Jun 8, 2026. Figures are from public GitHub metadata via [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval) and [Awesome-LLM-in-Social-Science's repository](https://github.com/ValueByte-AI/Awesome-LLM-in-Social-Science).

| | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | [Awesome-LLM-in-Social-Science](/tools/valuebyte-ai-awesome-llm-in-social-science.md) |
| --- | --- | --- |
| Tagline | Curated list for evaluation of large language models | Awesome papers involving LLMs in Social Science |
| Stars | 654 | 639 |
| Forks | 82 | 48 |
| Open issues | 44 | 0 |
| Language | - | - |
| Adopt for | Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. | Curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | MIT |
| Categories | Evaluation & Observability | Evaluation & Observability, Model Training |

## Trust and health

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

| | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | [Awesome-LLM-in-Social-Science](/tools/valuebyte-ai-awesome-llm-in-social-science.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Steady (60%) |
| Days since push | 246d | 49d |
| Open issues (now) | 44 | 0 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) | [trust report](/tools/valuebyte-ai-awesome-llm-in-social-science/trust.md) |

## Decision facts: Awesome-LLM-Eval

- **Pricing:** freemium - The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms.
- **Requirements:** The resources listed may vary in their own requirements, including software dependencies and hardware specifications.
- **Adopt for:** Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks.

## Decision facts: Awesome-LLM-in-Social-Science

- **Adopt for:** Curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more.

## Choose when

### Choose Awesome-LLM-Eval if…

- Pricing: The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms..
- Requirements: The resources listed may vary in their own requirements, including software dependencies and hardware specifications..
- Tags unique to Awesome-LLM-Eval: awesome-list, benchmark, datasets, evaluation.
- When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment.

### Choose Awesome-LLM-in-Social-Science if…

- Tags unique to Awesome-LLM-in-Social-Science: alignment, economics, llm-agent, policy.
- Also covers Model Training.
- Need to explore academic insights into LLM impacts on specific social areas

## When NOT to use Awesome-LLM-Eval

- You require real-time testing capabilities or interactive features; Awesome-LLM-Eval is a static resource list and not an interactive platform.
- If integration with specific third-party platforms or direct API access is necessary, since the repository predominantly serves as a reference point rather than an operational tool.

## When NOT to use Awesome-LLM-in-Social-Science

- Looking for a hands-on coding or practical implementation guide of LLMs
- In need of real-time data analysis tools for immediate social science research outcomes

## Common questions

### What is the difference between Awesome-LLM-Eval and Awesome-LLM-in-Social-Science?

Awesome-LLM-Eval: Curated list for evaluation of large language models. Awesome-LLM-in-Social-Science: Awesome papers involving LLMs in Social Science. See the comparison table for live GitHub stats and shared categories.

### When should I choose Awesome-LLM-Eval over Awesome-LLM-in-Social-Science?

Choose Awesome-LLM-Eval over Awesome-LLM-in-Social-Science when Pricing: The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms.; Requirements: The resources listed may vary in their own requirements, including software dependencies and hardware specifications.; Tags unique to Awesome-LLM-Eval: awesome-list, benchmark, datasets, evaluation; When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment.

### When should I choose Awesome-LLM-in-Social-Science over Awesome-LLM-Eval?

Choose Awesome-LLM-in-Social-Science over Awesome-LLM-Eval when Tags unique to Awesome-LLM-in-Social-Science: alignment, economics, llm-agent, policy; Also covers Model Training; Need to explore academic insights into LLM impacts on specific social areas.

### When should I avoid Awesome-LLM-Eval?

You require real-time testing capabilities or interactive features; Awesome-LLM-Eval is a static resource list and not an interactive platform. If integration with specific third-party platforms or direct API access is necessary, since the repository predominantly serves as a reference point rather than an operational tool.

### When should I avoid Awesome-LLM-in-Social-Science?

Looking for a hands-on coding or practical implementation guide of LLMs In need of real-time data analysis tools for immediate social science research outcomes

### Is Awesome-LLM-Eval or Awesome-LLM-in-Social-Science more popular on GitHub?

Awesome-LLM-Eval has more GitHub stars (654 vs 639). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-LLM-Eval and Awesome-LLM-in-Social-Science open source?

Yes - both are open-source projects on GitHub (Awesome-LLM-Eval: MIT, Awesome-LLM-in-Social-Science: MIT).

### Where can I find alternatives to Awesome-LLM-Eval or Awesome-LLM-in-Social-Science?

GraphCanon lists graph-backed alternatives at [Awesome-LLM-Eval alternatives](/tools/onejune2018-awesome-llm-eval/alternatives) and [Awesome-LLM-in-Social-Science alternatives](/tools/valuebyte-ai-awesome-llm-in-social-science/alternatives) ([Awesome-LLM-Eval markdown twin](/tools/onejune2018-awesome-llm-eval/alternatives.md), [Awesome-LLM-in-Social-Science markdown twin](/tools/valuebyte-ai-awesome-llm-in-social-science/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/onejune2018-awesome-llm-eval-vs-valuebyte-ai-awesome-llm-in-social-science.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Awesome-LLM-Eval or Awesome-LLM-in-Social-Science?

Awesome-LLM-Eval: Slowing. Awesome-LLM-in-Social-Science: Steady. 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 Awesome-LLM-Eval and Awesome-LLM-in-Social-Science?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLM-Eval trust report](/tools/onejune2018-awesome-llm-eval/trust); [Awesome-LLM-in-Social-Science trust report](/tools/valuebyte-ai-awesome-llm-in-social-science/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=onejune2018-awesome-llm-eval`](/api/graphcanon/graph?tool=onejune2018-awesome-llm-eval)
- 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/_