--- title: "Awesome-LLMs-ICLR-24 vs LongCite" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-thudm-longcite" tools: ["azminewasi-awesome-llms-iclr-24", "thudm-longcite"] --- # Awesome-LLMs-ICLR-24 vs LongCite *GraphCanon updated Aug 24, 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 LongCite if longCite is designed to enhance large language models by enabling them to generate fine-grained citations when answering queries with long context. [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. [LongCite](https://github.com/THUDM/LongCite) has 521 stars, 30 forks, and 9 open issues, last pushed Dec 31, 2024. Figures are from public GitHub metadata via [Awesome-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) and [LongCite's repository](https://github.com/THUDM/LongCite). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [LongCite](/tools/thudm-longcite.md) | | --- | --- | --- | | Tagline | Compilation of LLM papers from ICLR 2024 | Enabling LLMs to Generate Fine-grained Citations in Long-context QA | | Stars | 72 | 521 | | Forks | 5 | 30 | | Open issues | 0 | 9 | | 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. | LongCite is designed to enhance large language models by enabling them to generate fine-grained citations when answering queries with long context. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [LongCite](/tools/thudm-longcite.md) | | --- | --- | --- | | Days since push | 856d | 601d | | Open issues (now) | 0 | 9 | | Stars delta | Unknown | +1 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/thudm-longcite/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: LongCite - **Adopt for:** LongCite is designed to enhance large language models by enabling them to generate fine-grained citations when answering queries with long context. ## Choose when ### Choose Awesome-LLMs-ICLR-24 if… - License: Awesome-LLMs-ICLR-24 is MIT, LongCite 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, 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 LongCite if… - License: LongCite is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT. - Tags unique to LongCite: benchmark, citation-generation, fine-tuning, long-context. - When you require your LLM to provide detailed, well-cited responses in long-context scenarios. ## 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 LongCite - If your use case involves short queries or contexts that do not need extensive citations. - When the primary focus is on speed rather than detailed citation accuracy in responses. ## Common questions ### What is the difference between Awesome-LLMs-ICLR-24 and LongCite? Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMs-ICLR-24 over LongCite? Choose Awesome-LLMs-ICLR-24 over LongCite when License: Awesome-LLMs-ICLR-24 is MIT, LongCite 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, 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 LongCite over Awesome-LLMs-ICLR-24? Choose LongCite over Awesome-LLMs-ICLR-24 when License: LongCite is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT; Tags unique to LongCite: benchmark, citation-generation, fine-tuning, long-context; When you require your LLM to provide detailed, well-cited responses in long-context scenarios. ### 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 LongCite? If your use case involves short queries or contexts that do not need extensive citations. When the primary focus is on speed rather than detailed citation accuracy in responses. ### Is Awesome-LLMs-ICLR-24 or LongCite more popular on GitHub? LongCite has more GitHub stars (521 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMs-ICLR-24 and LongCite open source? Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, LongCite: Apache-2.0). ### Where can I find alternatives to Awesome-LLMs-ICLR-24 or LongCite? GraphCanon lists graph-backed alternatives at [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [LongCite alternatives](/tools/thudm-longcite/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/alternatives.md), [LongCite markdown twin](/tools/thudm-longcite/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-thudm-longcite.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 LongCite? Awesome-LLMs-ICLR-24: Dormant. LongCite: 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 LongCite? 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); [LongCite trust report](/tools/thudm-longcite/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/_