Comparison
LLMSys-PaperList vs Awesome-LLMs-ICLR-24
Verdict
Pick LLMSys-PaperList if lLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems; 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.
Markdown twin · LLMSys-PaperList alternatives · Awesome-LLMs-ICLR-24 alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | LLMSys-PaperList | Awesome-LLMs-ICLR-24 |
|---|---|---|
| Maintenance | Active (12d since push) As of 2w · github_public_v1 | Dormant (856d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 1w · 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
- LLMSys-PaperList
- Curated list of academic papers related to Large Language Model systems
- Awesome-LLMs-ICLR-24
- Compilation of LLM papers from ICLR 2024
Stars
- LLMSys-PaperList
- 2.2k
- Awesome-LLMs-ICLR-24
- 72
Forks
- LLMSys-PaperList
- 120
- Awesome-LLMs-ICLR-24
- 5
Open issues
- LLMSys-PaperList
- 1
- Awesome-LLMs-ICLR-24
- 0
Language
- LLMSys-PaperList
- Python
- Awesome-LLMs-ICLR-24
- -
Adopt for
- LLMSys-PaperList
- LLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems.
- 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.
Persona
- LLMSys-PaperList
- -
- Awesome-LLMs-ICLR-24
- -
Runtime
- LLMSys-PaperList
- -
- Awesome-LLMs-ICLR-24
- -
License
- LLMSys-PaperList
- (unknown)
- Awesome-LLMs-ICLR-24
- MIT
Last pushed
- LLMSys-PaperList
- Jul 25, 2026
- Awesome-LLMs-ICLR-24
- Apr 4, 2024
Categories
- LLMSys-PaperList
- Inference & Serving, LLM Frameworks, Model Training
- Awesome-LLMs-ICLR-24
- Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- LLMSys-PaperList
- Active (82%)
- Awesome-LLMs-ICLR-24
- Dormant (18%)
Days since push
- LLMSys-PaperList
- 12d
- Awesome-LLMs-ICLR-24
- 856d
Open issues (now)
- LLMSys-PaperList
- 1
- Awesome-LLMs-ICLR-24
- 0
Full report
- LLMSys-PaperList
- Trust report
- Awesome-LLMs-ICLR-24
- Trust report
Choose LLMSys-PaperList if…
- (repository does not specify hosting environment)
- Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers.
- - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems.
When NOT to use LLMSys-PaperList
- - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models.
- - When your primary need is documentation or code examples rather than academic papers and project insights.
- - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ
Choose Awesome-LLMs-ICLR-24 if…
- Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework.
- Also covers Developer Tools, Evaluation & Observability.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (AmberLJC/LLMSys-PaperList) · observed Aug 6, 2026
- GitHub forks (AmberLJC/LLMSys-PaperList) · observed Aug 6, 2026
- Last push (AmberLJC/LLMSys-PaperList) · observed Jul 25, 2026
- License file (unknown) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: LLMSys-PaperList 2.2k · Awesome-LLMs-ICLR-24 72 (synced Aug 6, 2026).
Common questions
- What is the difference between LLMSys-PaperList and Awesome-LLMs-ICLR-24?
- LLMSys-PaperList: Curated list of academic papers related to Large Language Model systems. Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLMSys-PaperList over Awesome-LLMs-ICLR-24?
- Choose LLMSys-PaperList over Awesome-LLMs-ICLR-24 when (repository does not specify hosting environment); Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers; - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems.
- When should I choose Awesome-LLMs-ICLR-24 over LLMSys-PaperList?
- Choose Awesome-LLMs-ICLR-24 over LLMSys-PaperList when Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Evaluation & Observability; 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 avoid LLMSys-PaperList?
- - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models. - When your primary need is documentation or code examples rather than academic papers and project insights. - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ
- 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.
- Is LLMSys-PaperList or Awesome-LLMs-ICLR-24 more popular on GitHub?
- LLMSys-PaperList has more GitHub stars (2,220 vs 72). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMSys-PaperList and Awesome-LLMs-ICLR-24 open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to LLMSys-PaperList or Awesome-LLMs-ICLR-24?
- GraphCanon lists graph-backed alternatives at LLMSys-PaperList alternatives and Awesome-LLMs-ICLR-24 alternatives (LLMSys-PaperList markdown twin, Awesome-LLMs-ICLR-24 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, LLMSys-PaperList or Awesome-LLMs-ICLR-24?
- LLMSys-PaperList: Active. Awesome-LLMs-ICLR-24: 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 LLMSys-PaperList and Awesome-LLMs-ICLR-24?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMSys-PaperList trust report; Awesome-LLMs-ICLR-24 trust report.