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Home/Compare/Awesome-LLMs-ICLR-24 vs ml-surveys

Comparison

Awesome-LLMs-ICLR-24 vs ml-surveys

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 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.

Markdown twin · Awesome-LLMs-ICLR-24 alternatives · ml-surveys alternatives

GraphCanon updated 1w

Awesome-LLMs-ICLR-24 logo

Awesome-LLMs-ICLR-24

azminewasi/Awesome-LLMs-ICLR-24

★ 72pushed Apr 4, 2024
vs
ml-surveys logo

ml-surveys

eugeneyan/ml-surveys

★ 2.9kpushed Mar 17, 2023

Trust & integrity

SignalAwesome-LLMs-ICLR-24ml-surveys
Maintenance
Dormant (856d since push)
As of 1w · github_public_v1
Dormant (1223d since push)
As of 1mo · github_public_v1
Provenance
Not a fork · Personal account
As of 1w · github_public_v1
Not a fork · Personal account
As of 1mo · 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
Awesome-LLMs-ICLR-24 trust reportml-surveys trust reportTrust methodology

Tagline

Awesome-LLMs-ICLR-24
Compilation of LLM papers from ICLR 2024
ml-surveys
Survey papers summarizing advances in various AI domains

Stars

Awesome-LLMs-ICLR-24
72
ml-surveys
2.9k

Forks

Awesome-LLMs-ICLR-24
5
ml-surveys
291

Open issues

Awesome-LLMs-ICLR-24
0
ml-surveys
2

Language

Awesome-LLMs-ICLR-24
-
ml-surveys
-

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.
ml-surveys
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.

Persona

Awesome-LLMs-ICLR-24
-
ml-surveys
-

Runtime

Awesome-LLMs-ICLR-24
-
ml-surveys
-

License

Awesome-LLMs-ICLR-24
MIT
ml-surveys
MIT

Last pushed

Awesome-LLMs-ICLR-24
Apr 4, 2024
ml-surveys
Mar 17, 2023

Categories

Awesome-LLMs-ICLR-24
Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
ml-surveys
Computer Vision, Evaluation & Observability, Model Training

Trust and health

Days since push

Awesome-LLMs-ICLR-24
856d
ml-surveys
1223d

Open issues (now)

Awesome-LLMs-ICLR-24
0
ml-surveys
2

Full report

Awesome-LLMs-ICLR-24
Trust report
ml-surveys
Trust report

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, 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.
GitHubDetails

Choose ml-surveys if…

  • Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning.
  • Also covers Computer Vision.
  • When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning

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
GitHubDetails

Explore

Awesome-LLMs-ICLR-24 trust report →ml-surveys trust report →Developer Tools category →Evaluation & Observability category →Inference & Serving category →LLM Frameworks category →Model Training category →Computer Vision category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: Awesome-LLMs-ICLR-24 72 · ml-surveys 2.9k (synced Aug 8, 2026).

Common questions

What is the difference between Awesome-LLMs-ICLR-24 and ml-surveys?
Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. ml-surveys: Survey papers summarizing advances in various AI domains. See the comparison table for live GitHub stats and shared categories.
When should I choose Awesome-LLMs-ICLR-24 over ml-surveys?
Choose Awesome-LLMs-ICLR-24 over ml-surveys when 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 ml-surveys over Awesome-LLMs-ICLR-24?
Choose ml-surveys over Awesome-LLMs-ICLR-24 when Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning; 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 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 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
Is Awesome-LLMs-ICLR-24 or ml-surveys more popular on GitHub?
ml-surveys has more GitHub stars (2,902 vs 72). Stars measure visibility, not whether either tool fits your constraints.
Are Awesome-LLMs-ICLR-24 and ml-surveys open source?
Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, ml-surveys: MIT).
Where can I find alternatives to Awesome-LLMs-ICLR-24 or ml-surveys?
GraphCanon lists graph-backed alternatives at Awesome-LLMs-ICLR-24 alternatives and ml-surveys alternatives (Awesome-LLMs-ICLR-24 markdown twin, ml-surveys 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 ml-surveys?
Awesome-LLMs-ICLR-24: Dormant. ml-surveys: 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 ml-surveys?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLMs-ICLR-24 trust report; ml-surveys trust report.

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