Comparison
Awesome-LLMs-ICLR-24 vs ReNeLLM
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 ReNeLLM if reNeLLM is an implementation of generalized nested jailbreak prompts targeting large language models such as gpt-3.5-turbo and claude-v2.
Markdown twin · Awesome-LLMs-ICLR-24 alternatives · ReNeLLM alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-LLMs-ICLR-24 | ReNeLLM |
|---|---|---|
| Maintenance | Dormant (856d since push) As of 2w · github_public_v1 | Slowing (336d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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
- ReNeLLM
- Implementation of generalized nested jailbreak prompts targeting large language models.
Stars
- Awesome-LLMs-ICLR-24
- 72
- ReNeLLM
- 163
Forks
- Awesome-LLMs-ICLR-24
- 5
- ReNeLLM
- 17
Open issues
- Awesome-LLMs-ICLR-24
- 0
- ReNeLLM
- 0
Language
- Awesome-LLMs-ICLR-24
- -
- ReNeLLM
- Python
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.
- ReNeLLM
- ReNeLLM is an implementation of generalized nested jailbreak prompts targeting large language models such as gpt-3.5-turbo and claude-v2.
Persona
- Awesome-LLMs-ICLR-24
- -
- ReNeLLM
- -
Runtime
- Awesome-LLMs-ICLR-24
- -
- ReNeLLM
- -
License
- Awesome-LLMs-ICLR-24
- MIT
- ReNeLLM
- MIT
Last pushed
- Awesome-LLMs-ICLR-24
- Apr 4, 2024
- ReNeLLM
- Sep 2, 2025
Categories
- Awesome-LLMs-ICLR-24
- Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
- ReNeLLM
- Evaluation & Observability, Inference & Serving
Trust and health
Maintenance
- Awesome-LLMs-ICLR-24
- Dormant (18%)
- ReNeLLM
- Slowing (36%)
Days since push
- Awesome-LLMs-ICLR-24
- 856d
- ReNeLLM
- 336d
Owner type
- Awesome-LLMs-ICLR-24
- User
- ReNeLLM
- Organization
OSV dependency advisories
- Awesome-LLMs-ICLR-24
- No lockfile (source not queried)
- ReNeLLM
- Published findings
Full report
- Awesome-LLMs-ICLR-24
- Trust report
- ReNeLLM
- 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, LLM Frameworks, Model Training.
- 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 ReNeLLM if…
- Tags unique to ReNeLLM: api interaction, jailbreak prompts, language model evaluation, model reliability assessment.
- When you aim to evaluate the susceptibility of LLMs like gpt-3.5-turbo and claude-v2 to deception or jailbroken prompts.
- More GitHub stars (163 vs 72) - visibility, not fit.
When NOT to use ReNeLLM
- When you wish to develop applications that strictly adhere to ethical guidelines and do not involve the testing of harmful prompts.
- If your focus is on building production-ready LLM-based services without interest in evaluating security or adversarial aspects of these models.
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 (NJUNLP/ReNeLLM) · observed Aug 5, 2026
- GitHub forks (NJUNLP/ReNeLLM) · observed Aug 5, 2026
- Last push (NJUNLP/ReNeLLM) · observed Sep 2, 2025
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLMs-ICLR-24 72 · ReNeLLM 163 (synced Aug 8, 2026).
Common questions
- What is the difference between Awesome-LLMs-ICLR-24 and ReNeLLM?
- Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. ReNeLLM: Implementation of generalized nested jailbreak prompts targeting large language models.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-LLMs-ICLR-24 over ReNeLLM?
- Choose Awesome-LLMs-ICLR-24 over ReNeLLM when Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, LLM Frameworks, Model Training; 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 ReNeLLM over Awesome-LLMs-ICLR-24?
- Choose ReNeLLM over Awesome-LLMs-ICLR-24 when Tags unique to ReNeLLM: api interaction, jailbreak prompts, language model evaluation, model reliability assessment; When you aim to evaluate the susceptibility of LLMs like gpt-3.5-turbo and claude-v2 to deception or jailbroken prompts; More GitHub stars (163 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 ReNeLLM?
- When you wish to develop applications that strictly adhere to ethical guidelines and do not involve the testing of harmful prompts. If your focus is on building production-ready LLM-based services without interest in evaluating security or adversarial aspects of these models.
- Is Awesome-LLMs-ICLR-24 or ReNeLLM more popular on GitHub?
- ReNeLLM has more GitHub stars (163 vs 72). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLMs-ICLR-24 and ReNeLLM open source?
- Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, ReNeLLM: MIT).
- Where can I find alternatives to Awesome-LLMs-ICLR-24 or ReNeLLM?
- GraphCanon lists graph-backed alternatives at Awesome-LLMs-ICLR-24 alternatives and ReNeLLM alternatives (Awesome-LLMs-ICLR-24 markdown twin, ReNeLLM 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 ReNeLLM?
- Awesome-LLMs-ICLR-24: Dormant. ReNeLLM: Slowing. 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 ReNeLLM?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLMs-ICLR-24 trust report; ReNeLLM trust report.