--- title: "LLMSurvey vs LongCite" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rucaibox-llmsurvey-vs-thudm-longcite" tools: ["rucaibox-llmsurvey", "thudm-longcite"] --- # LLMSurvey vs LongCite *GraphCanon updated Aug 24, 2026* ## Verdict Pick LLMSurvey if lLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训; 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. [LLMSurvey](https://arxiv.org/abs/2303.18223) reports 12k GitHub stars, 931 forks, and 30 open issues, last pushed Mar 11, 2025. [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 [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey) and [LongCite's repository](https://github.com/THUDM/LongCite). | | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | [LongCite](/tools/thudm-longcite.md) | | --- | --- | --- | | Tagline | A comprehensive collection of papers and resources related to Large Language Models. | Enabling LLMs to Generate Fine-grained Citations in Long-context QA | | Stars | 12,205 | 521 | | Forks | 931 | 30 | | Open issues | 30 | 9 | | Language | Python | Python | | Adopt for | LLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训 | 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 | The license for LLMSurvey is unknown based on the provided repository information. | Apache-2.0 | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | [LongCite](/tools/thudm-longcite.md) | | --- | --- | --- | | Days since push | 523d | 601d | | Open issues (now) | 30 | 9 | | Stars delta | +18 (30d) | +1 (30d) | | Full report | [trust report](/tools/rucaibox-llmsurvey/trust.md) | [trust report](/tools/thudm-longcite/trust.md) | ## Decision facts: LLMSurvey - **Pricing:** freemium - Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage - **Adopt for:** LLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训 - **License detail:** The license for LLMSurvey is unknown based on the provided repository information. ## 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 LLMSurvey if… - Pricing: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage. - Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, large language models. - You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series models. ### Choose LongCite if… - 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. - Leaner open-issue backlog (9). ## When NOT to use LLMSurvey - You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers. - Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how ## 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 LLMSurvey and LongCite? LLMSurvey: A comprehensive collection of papers and resources related to Large Language Models.. 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 LLMSurvey over LongCite? Choose LLMSurvey over LongCite when Pricing: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage; Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, large language models; You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series models. ### When should I choose LongCite over LLMSurvey? Choose LongCite over LLMSurvey when 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; Leaner open-issue backlog (9). ### When should I avoid LLMSurvey? You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers. Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how ### 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 LLMSurvey or LongCite more popular on GitHub? LLMSurvey has more GitHub stars (12,205 vs 521). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMSurvey and LongCite open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLMSurvey or LongCite? GraphCanon lists graph-backed alternatives at [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) and [LongCite alternatives](/tools/thudm-longcite/alternatives) ([LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/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/rucaibox-llmsurvey-vs-thudm-longcite.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMSurvey or LongCite? LLMSurvey: 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 LLMSurvey and LongCite? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust); [LongCite trust report](/tools/thudm-longcite/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=rucaibox-llmsurvey`](/api/graphcanon/graph?tool=rucaibox-llmsurvey) - 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/_