--- title: "LLMSurvey vs IB4LLMs" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rucaibox-llmsurvey-vs-zichuan-liu-ib4llms" tools: ["rucaibox-llmsurvey", "zichuan-liu-ib4llms"] --- # LLMSurvey vs IB4LLMs *GraphCanon updated Jul 12, 2026* ## Verdict Pick LLMSurvey 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; pick IB4LLMs when tags unique to IB4LLMs: adversarial prompts defense, information bottleneck, jailbreak defense, llms protection. [LLMSurvey](https://arxiv.org/abs/2303.18223) reports 12k GitHub stars, 935 forks, and 30 open issues, last pushed Mar 11, 2025. [IB4LLMs](https://zichuan-liu.github.io/projects/IBProtector/index.html) has 25 stars, 2 forks, and 4 open issues, last pushed Nov 7, 2024. Figures are from public GitHub metadata via [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey) and [IB4LLMs's repository](https://github.com/zichuan-liu/IB4LLMs). | | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | [IB4LLMs](/tools/zichuan-liu-ib4llms.md) | | --- | --- | --- | | Tagline | A comprehensive collection of papers and resources related to Large Language Models. | Protecting Your LLMs with Information Bottleneck | | Stars | 12,187 | 25 | | Forks | 935 | 2 | | Open issues | 30 | 4 | | 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 | - | | Persona | - | - | | Runtime | - | - | | License | The license for LLMSurvey is unknown based on the provided repository information. | - | | 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) | [IB4LLMs](/tools/zichuan-liu-ib4llms.md) | | --- | --- | --- | | Days since push | 487d | 611d | | Open issues (now) | 30 | 4 | | Owner type | Organization | User | | Security scan | No lockfile | 77 low (77 low) | | Full report | [trust report](/tools/rucaibox-llmsurvey/trust.md) | [trust report](/tools/zichuan-liu-ib4llms/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. ## 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 IB4LLMs if… - Tags unique to IB4LLMs: adversarial prompts defense, information bottleneck, jailbreak defense, llms protection. - Leaner open-issue backlog (4). ## 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 IB4LLMs - Last GitHub push was 612 days ago (dormant maintenance, Nov 7, 2024). Validate activity before betting a new project on IB4LLMs. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between LLMSurvey and IB4LLMs? LLMSurvey: A comprehensive collection of papers and resources related to Large Language Models.. IB4LLMs: Protecting Your LLMs with Information Bottleneck. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMSurvey over IB4LLMs? Choose LLMSurvey over IB4LLMs 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 IB4LLMs over LLMSurvey? Choose IB4LLMs over LLMSurvey when Tags unique to IB4LLMs: adversarial prompts defense, information bottleneck, jailbreak defense, llms protection; Leaner open-issue backlog (4). ### 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 IB4LLMs? Last GitHub push was 612 days ago (dormant maintenance, Nov 7, 2024). Validate activity before betting a new project on IB4LLMs. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is LLMSurvey or IB4LLMs more popular on GitHub? LLMSurvey has more GitHub stars (12,187 vs 25). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMSurvey and IB4LLMs open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLMSurvey or IB4LLMs? GraphCanon lists graph-backed alternatives at [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) and [IB4LLMs alternatives](/tools/zichuan-liu-ib4llms/alternatives) ([LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/alternatives.md), [IB4LLMs markdown twin](/tools/zichuan-liu-ib4llms/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-zichuan-liu-ib4llms.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMSurvey or IB4LLMs? LLMSurvey: Dormant. IB4LLMs: 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 IB4LLMs? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust); [IB4LLMs trust report](/tools/zichuan-liu-ib4llms/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/_