--- title: "LLMSurvey vs Chain-of-ThoughtsPapers" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rucaibox-llmsurvey-vs-timothyxxx-chain-of-thoughtspapers" tools: ["rucaibox-llmsurvey", "timothyxxx-chain-of-thoughtspapers"] --- # LLMSurvey vs Chain-of-ThoughtsPapers *GraphCanon updated Oct 11, 2026* ## Verdict Pick LLMSurvey if lLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning; pick Chain-of-ThoughtsPapers if chain-of-ThoughtsPapers curates critical research on chain-of-thought reasoning in large language models, aimed at enhancing a model's ability to perform logical reasoning through iterative step-by-step analyses. [LLMSurvey](https://arxiv.org/abs/2303.18223) reports 12k GitHub stars, 930 forks, and 30 open issues, last pushed Mar 11, 2025. [Chain-of-ThoughtsPapers](https://github.com/Timothyxxx/Chain-of-ThoughtsPapers) has 2.1k stars, 143 forks, and 0 open issues, last pushed Oct 5, 2023. Figures are from public GitHub metadata via [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey) and [Chain-of-ThoughtsPapers's repository](https://github.com/Timothyxxx/Chain-of-ThoughtsPapers). | | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | [Chain-of-ThoughtsPapers](/tools/timothyxxx-chain-of-thoughtspapers.md) | | --- | --- | --- | | Tagline | A collection of papers and resources related to Large Language Models | A curated list of papers exploring chain-of-thought reasoning in large language models. | | Stars | 12,216 | 2,104 | | Forks | 930 | 143 | | Open issues | 30 | 0 | | Language | Python | - | | Adopt for | LLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning. | Chain-of-ThoughtsPapers curates critical research on chain-of-thought reasoning in large language models, aimed at enhancing a model's ability to perform logical reasoning through iterative step-by-step analyses. | | Persona | - | end user agent | | Runtime | - | - | | License | The license information for LLMSurvey is unknown. | - | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | [Chain-of-ThoughtsPapers](/tools/timothyxxx-chain-of-thoughtspapers.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Archived (8%) | | Days since push | 555d | 1102d | | Archived on GitHub | No | Yes | | Open issues (now) | 30 | 0 | | Stars delta | +11 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/rucaibox-llmsurvey/trust.md) | [trust report](/tools/timothyxxx-chain-of-thoughtspapers/trust.md) | ## Decision facts: LLMSurvey - **Pricing:** freemium - The repository is free to access, but the Chinese book is available for purchase. - **Requirements:** Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book. - **Adopt for:** LLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning. - **License detail:** The license information for LLMSurvey is unknown. ## Decision facts: Chain-of-ThoughtsPapers - **Adopt for:** Chain-of-ThoughtsPapers curates critical research on chain-of-thought reasoning in large language models, aimed at enhancing a model's ability to perform logical reasoning through iterative step-by-step analyses. - **Persona:** end user agent ## Choose when ### Choose LLMSurvey if… - Pricing: The repository is free to access, but the Chinese book is available for purchase.. - Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book.. - Tags unique to LLMSurvey: chatgpt, instruction-tuning, llm, llms. - Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning. ### Choose Chain-of-ThoughtsPapers if… - Tags unique to Chain-of-ThoughtsPapers: codex, gpt-3, palm, prompt-learning. - When you need insights into foundational and cutting-edge research on how language models can be prompted or structured to reason logically. - Leaner open-issue backlog (0). ## When NOT to use LLMSurvey - Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys. - Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources. ## When NOT to use Chain-of-ThoughtsPapers - If your focus is on unrelated areas such as image processing or speech recognition, where chain-of-thought reasoning in LLMs does not directly play a role. - This repository focuses on research and theoretical foundations, not ready-to-use software libraries or codebases, making it less suitable for projects that require immediate practical coding implementations. - In scenarios necessitating alternative approaches to language model training which do not emphasize step-by-step reasoning, such as models trained purely for pattern recognition without emphasis on a - what_is_missing ## Common questions ### What is the difference between LLMSurvey and Chain-of-ThoughtsPapers? LLMSurvey: A collection of papers and resources related to Large Language Models. Chain-of-ThoughtsPapers: A curated list of papers exploring chain-of-thought reasoning in large language models.. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMSurvey over Chain-of-ThoughtsPapers? Choose LLMSurvey over Chain-of-ThoughtsPapers when Pricing: The repository is free to access, but the Chinese book is available for purchase.; Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book.; Tags unique to LLMSurvey: chatgpt, instruction-tuning, llm, llms; Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning. ### When should I choose Chain-of-ThoughtsPapers over LLMSurvey? Choose Chain-of-ThoughtsPapers over LLMSurvey when Tags unique to Chain-of-ThoughtsPapers: codex, gpt-3, palm, prompt-learning; When you need insights into foundational and cutting-edge research on how language models can be prompted or structured to reason logically; Leaner open-issue backlog (0). ### When should I avoid LLMSurvey? Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys. Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources. ### When should I avoid Chain-of-ThoughtsPapers? If your focus is on unrelated areas such as image processing or speech recognition, where chain-of-thought reasoning in LLMs does not directly play a role. This repository focuses on research and theoretical foundations, not ready-to-use software libraries or codebases, making it less suitable for projects that require immediate practical coding implementations. In scenarios necessitating alternative approaches to language model training which do not emphasize step-by-step reasoning, such as models trained purely for pattern recognition without emphasis on a what_is_missing ### Is LLMSurvey or Chain-of-ThoughtsPapers more popular on GitHub? LLMSurvey has more GitHub stars (12,216 vs 2,104). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMSurvey and Chain-of-ThoughtsPapers open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLMSurvey or Chain-of-ThoughtsPapers? GraphCanon lists graph-backed alternatives at [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) and [Chain-of-ThoughtsPapers alternatives](/tools/timothyxxx-chain-of-thoughtspapers/alternatives) ([LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/alternatives.md), [Chain-of-ThoughtsPapers markdown twin](/tools/timothyxxx-chain-of-thoughtspapers/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-timothyxxx-chain-of-thoughtspapers.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMSurvey or Chain-of-ThoughtsPapers? LLMSurvey: Dormant. Chain-of-ThoughtsPapers: Archived. 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 Chain-of-ThoughtsPapers? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust); [Chain-of-ThoughtsPapers trust report](/tools/timothyxxx-chain-of-thoughtspapers/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/_