Comparison
Awesome-LLMs-ICLR-24 vs awesome-automl-papers
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-automl-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search.
Markdown twin · Awesome-LLMs-ICLR-24 alternatives · awesome-automl-papers alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-LLMs-ICLR-24 | awesome-automl-papers |
|---|---|---|
| Maintenance | Dormant (856d since push) As of 2w · github_public_v1 | Dormant (784d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 3w · 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
- Awesome-LLMs-ICLR-24
- Compilation of LLM papers from ICLR 2024
- awesome-automl-papers
- A curated list of automated machine learning papers and resources.
Stars
- Awesome-LLMs-ICLR-24
- 72
- awesome-automl-papers
- 4.2k
Forks
- Awesome-LLMs-ICLR-24
- 5
- awesome-automl-papers
- 678
Open issues
- Awesome-LLMs-ICLR-24
- 0
- awesome-automl-papers
- 2
Language
- Awesome-LLMs-ICLR-24
- -
- awesome-automl-papers
- -
Adopt for
- Awesome-LLMs-ICLR-24
- 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.
- awesome-automl-papers
- awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search.
Persona
- Awesome-LLMs-ICLR-24
- -
- awesome-automl-papers
- -
Runtime
- Awesome-LLMs-ICLR-24
- -
- awesome-automl-papers
- -
License
- Awesome-LLMs-ICLR-24
- MIT
- awesome-automl-papers
- Apache-2.0
Last pushed
- Awesome-LLMs-ICLR-24
- Apr 4, 2024
- awesome-automl-papers
- Jun 11, 2024
Categories
- Awesome-LLMs-ICLR-24
- Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
- awesome-automl-papers
- Evaluation & Observability, Model Training
Trust and health
Days since push
- Awesome-LLMs-ICLR-24
- 856d
- awesome-automl-papers
- 784d
Open issues (now)
- Awesome-LLMs-ICLR-24
- 0
- awesome-automl-papers
- 2
Full report
- Awesome-LLMs-ICLR-24
- Trust report
- awesome-automl-papers
- Trust report
Choose Awesome-LLMs-ICLR-24 if…
- License: Awesome-LLMs-ICLR-24 is MIT, awesome-automl-papers is Apache-2.0.
- Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework.
- 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 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.
Choose awesome-automl-papers if…
- License: awesome-automl-papers is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT.
- Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search.
- When you need a curated list of academic materials to research or learn about AutoML technologies
When NOT to use awesome-automl-papers
- If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources
- When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (azminewasi/Awesome-LLMs-ICLR-24) · observed Aug 8, 2026
- GitHub forks (azminewasi/Awesome-LLMs-ICLR-24) · observed Aug 8, 2026
- Last push (azminewasi/Awesome-LLMs-ICLR-24) · observed Apr 4, 2024
- License file (MIT) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (hibayesian/awesome-automl-papers) · observed Aug 4, 2026
- GitHub forks (hibayesian/awesome-automl-papers) · observed Aug 4, 2026
- Last push (hibayesian/awesome-automl-papers) · observed Jun 11, 2024
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLMs-ICLR-24 72 · awesome-automl-papers 4.2k (synced Aug 8, 2026).
Common questions
- What is the difference between Awesome-LLMs-ICLR-24 and awesome-automl-papers?
- Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. awesome-automl-papers: A curated list of automated machine learning papers and resources.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-LLMs-ICLR-24 over awesome-automl-papers?
- Choose Awesome-LLMs-ICLR-24 over awesome-automl-papers when License: Awesome-LLMs-ICLR-24 is MIT, awesome-automl-papers is Apache-2.0; Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; 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-automl-papers over Awesome-LLMs-ICLR-24?
- Choose awesome-automl-papers over Awesome-LLMs-ICLR-24 when License: awesome-automl-papers is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT; Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; When you need a curated list of academic materials to research or learn about AutoML technologies.
- 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-automl-papers?
- If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers
- Is Awesome-LLMs-ICLR-24 or awesome-automl-papers more popular on GitHub?
- awesome-automl-papers has more GitHub stars (4,155 vs 72). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLMs-ICLR-24 and awesome-automl-papers open source?
- Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, awesome-automl-papers: Apache-2.0).
- Where can I find alternatives to Awesome-LLMs-ICLR-24 or awesome-automl-papers?
- GraphCanon lists graph-backed alternatives at Awesome-LLMs-ICLR-24 alternatives and awesome-automl-papers alternatives (Awesome-LLMs-ICLR-24 markdown twin, awesome-automl-papers 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, Awesome-LLMs-ICLR-24 or awesome-automl-papers?
- Awesome-LLMs-ICLR-24: Dormant. awesome-automl-papers: 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 Awesome-LLMs-ICLR-24 and awesome-automl-papers?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLMs-ICLR-24 trust report; awesome-automl-papers trust report.