--- title: "Awesome-LLMs-ICLR-24 vs Awesome-LLM-in-Social-Science" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-valuebyte-ai-awesome-llm-in-social-science" tools: ["azminewasi-awesome-llms-iclr-24", "valuebyte-ai-awesome-llm-in-social-science"] --- # Awesome-LLMs-ICLR-24 vs Awesome-LLM-in-Social-Science *GraphCanon updated Aug 8, 2026* ## Verdict Pick Awesome-LLMs-ICLR-24 if awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024; 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-LLMs-ICLR-24](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) reports 72 GitHub stars, 5 forks, and 0 open issues, last pushed Apr 4, 2024. [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-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) and [Awesome-LLM-in-Social-Science's repository](https://github.com/ValueByte-AI/Awesome-LLM-in-Social-Science). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [Awesome-LLM-in-Social-Science](/tools/valuebyte-ai-awesome-llm-in-social-science.md) | | --- | --- | --- | | Tagline | Compilation of LLM papers from ICLR 2024 | Awesome papers involving LLMs in Social Science | | Stars | 72 | 639 | | Forks | 5 | 48 | | Open issues | 0 | 0 | | Language | - | - | | Adopt for | Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024. | Curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [Awesome-LLM-in-Social-Science](/tools/valuebyte-ai-awesome-llm-in-social-science.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 856d | 49d | | Owner type | User | Organization | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/valuebyte-ai-awesome-llm-in-social-science/trust.md) | ## Decision facts: Awesome-LLMs-ICLR-24 - **Adopt for:** Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024. ## 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-LLMs-ICLR-24 if… - Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-framework, llm-inference, llm-training. - Also covers Developer Tools, Inference & Serving, LLM Frameworks. - If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights. ### Choose Awesome-LLM-in-Social-Science if… - Tags unique to Awesome-LLM-in-Social-Science: alignment, economics, large language models, policy. - Need to explore academic insights into LLM impacts on specific social areas - More GitHub stars (639 vs 72) - visibility, not fit. ## When NOT to use Awesome-LLMs-ICLR-24 - If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. - For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis. ## 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-LLMs-ICLR-24 and Awesome-LLM-in-Social-Science? Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. 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-LLMs-ICLR-24 over Awesome-LLM-in-Social-Science? Choose Awesome-LLMs-ICLR-24 over Awesome-LLM-in-Social-Science when Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-framework, llm-inference, llm-training; Also covers Developer Tools, Inference & Serving, LLM Frameworks; If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights. ### When should I choose Awesome-LLM-in-Social-Science over Awesome-LLMs-ICLR-24? Choose Awesome-LLM-in-Social-Science over Awesome-LLMs-ICLR-24 when Tags unique to Awesome-LLM-in-Social-Science: alignment, economics, large language models, policy; Need to explore academic insights into LLM impacts on specific social areas; More GitHub stars (639 vs 72) - visibility, not fit. ### When should I avoid Awesome-LLMs-ICLR-24? If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis. ### 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-LLMs-ICLR-24 or Awesome-LLM-in-Social-Science more popular on GitHub? Awesome-LLM-in-Social-Science has more GitHub stars (639 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMs-ICLR-24 and Awesome-LLM-in-Social-Science open source? Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, Awesome-LLM-in-Social-Science: MIT). ### Where can I find alternatives to Awesome-LLMs-ICLR-24 or Awesome-LLM-in-Social-Science? GraphCanon lists graph-backed alternatives at [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [Awesome-LLM-in-Social-Science alternatives](/tools/valuebyte-ai-awesome-llm-in-social-science/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/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/azminewasi-awesome-llms-iclr-24-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-LLMs-ICLR-24 or Awesome-LLM-in-Social-Science? Awesome-LLMs-ICLR-24: Dormant. 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-LLMs-ICLR-24 and Awesome-LLM-in-Social-Science? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLMs-ICLR-24 trust report](/tools/azminewasi-awesome-llms-iclr-24/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=azminewasi-awesome-llms-iclr-24`](/api/graphcanon/graph?tool=azminewasi-awesome-llms-iclr-24) - 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/_