--- title: "Awesome-LLMs-ICLR-24 vs ReNeLLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-njunlp-renellm" tools: ["azminewasi-awesome-llms-iclr-24", "njunlp-renellm"] --- # Awesome-LLMs-ICLR-24 vs ReNeLLM *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 ReNeLLM if reNeLLM is an implementation of generalized nested jailbreak prompts targeting large language models such as gpt-3.5-turbo and claude-v2. [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. [ReNeLLM](https://github.com/NJUNLP/ReNeLLM) has 163 stars, 17 forks, and 0 open issues, last pushed Sep 2, 2025. Figures are from public GitHub metadata via [Awesome-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) and [ReNeLLM's repository](https://github.com/NJUNLP/ReNeLLM). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [ReNeLLM](/tools/njunlp-renellm.md) | | --- | --- | --- | | Tagline | Compilation of LLM papers from ICLR 2024 | Implementation of generalized nested jailbreak prompts targeting large language models. | | Stars | 72 | 163 | | Forks | 5 | 17 | | Open issues | 0 | 0 | | Language | - | Python | | 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. | ReNeLLM is an implementation of generalized nested jailbreak prompts targeting large language models such as gpt-3.5-turbo and claude-v2. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [ReNeLLM](/tools/njunlp-renellm.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 856d | 336d | | Owner type | User | Organization | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/njunlp-renellm/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: ReNeLLM - **Adopt for:** ReNeLLM is an implementation of generalized nested jailbreak prompts targeting large language models such as gpt-3.5-turbo and claude-v2. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [ReNeLLM alternatives](/tools/njunlp-renellm/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/alternatives.md), [ReNeLLM markdown twin](/tools/njunlp-renellm/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-njunlp-renellm.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 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](/tools/azminewasi-awesome-llms-iclr-24/trust); [ReNeLLM trust report](/tools/njunlp-renellm/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/_