--- title: "evidentiality_qa vs LLMSurvey" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/akariasai-evidentiality-qa-vs-rucaibox-llmsurvey" tools: ["akariasai-evidentiality-qa", "rucaibox-llmsurvey"] --- # evidentiality_qa vs LLMSurvey *GraphCanon updated Sep 20, 2026* ## Verdict Pick evidentiality_qa if evidentiality_qa is a Python-based model that integrates evidentiality into training for knowledge-intensive NLP tasks, such as open question answering, fact verification, and knowledge-enhanced dialogue, using multi-tas; 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. [evidentiality_qa](https://github.com/AkariAsai/evidentiality_qa) reports 44 GitHub stars, 0 forks, and 2 open issues, last pushed Dec 25, 2022. [LLMSurvey](https://arxiv.org/abs/2303.18223) has 12k stars, 930 forks, and 30 open issues, last pushed Mar 11, 2025. Figures are from public GitHub metadata via [evidentiality_qa's repository](https://github.com/AkariAsai/evidentiality_qa) and [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey). | | [evidentiality_qa](/tools/akariasai-evidentiality-qa.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Tagline | Evidentiality-guided Generator for Knowledge-Intensive NLP Tasks | A collection of papers and resources related to Large Language Models | | Stars | 44 | 12,216 | | Forks | 0 | 930 | | Open issues | 2 | 30 | | Language | Python | Python | | Adopt for | Evidentiality_qa is a Python-based model that integrates evidentiality into training for knowledge-intensive NLP tasks, such as open question answering, fact verification, and knowledge-enhanced dialogue, using multi-tas | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT License | The license information for LLMSurvey is unknown. | | Categories | Data & Retrieval, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [evidentiality_qa](/tools/akariasai-evidentiality-qa.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Days since push | 1362d | 555d | | Open issues (now) | 2 | 30 | | Stars delta | 0 (30d) | +11 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/akariasai-evidentiality-qa/trust.md) | [trust report](/tools/rucaibox-llmsurvey/trust.md) | ## Decision facts: evidentiality_qa - **Requirements:** Min 8 GB RAM; The model requires access to silver evidentiality data for training.; Training involves a multi-step process including training a base Fusion-in-Decoder model and a separate evidentiality labeling model. - **Adopt for:** Evidentiality_qa is a Python-based model that integrates evidentiality into training for knowledge-intensive NLP tasks, such as open question answering, fact verification, and knowledge-enhanced dialogue, using multi-tas - **License detail:** MIT License ## 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. ## Choose when ### Choose evidentiality_qa if… - Requirements: Min 8 GB RAM; The model requires access to silver evidentiality data for training.; Training involves a multi-step process including training a base Fusion-in-Decoder model and a separate evidentiality labeling model.. - Tags unique to evidentiality_qa: evidentiality, fine-tuned models, knowledge-intensive tasks, multi-task learning. - Also covers Data & Retrieval. - Use evidentiality_qa when you need to improve performance on knowledge-intensive NLP tasks that require the model to generate answers based on evidence from retrieved passages. ### 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: chain-of-thought, chatgpt, in-context-learning, instruction-tuning. - Also covers LLM Frameworks. - 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 NOT to use evidentiality_qa - Avoid using evidentiality_qa if your task does not involve knowledge-intensive NLP tasks that require evidence-based reasoning. - Do not use evidentiality_qa if you lack the necessary silver evidentiality data for training, as the model relies on this data to predict evidentiality and generate answers. ## 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. ## Common questions ### What is the difference between evidentiality_qa and LLMSurvey? evidentiality_qa: Evidentiality-guided Generator for Knowledge-Intensive NLP Tasks. LLMSurvey: A collection of papers and resources related to Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose evidentiality_qa over LLMSurvey? Choose evidentiality_qa over LLMSurvey when Requirements: Min 8 GB RAM; The model requires access to silver evidentiality data for training.; Training involves a multi-step process including training a base Fusion-in-Decoder model and a separate evidentiality labeling model.; Tags unique to evidentiality_qa: evidentiality, fine-tuned models, knowledge-intensive tasks, multi-task learning; Also covers Data & Retrieval; Use evidentiality_qa when you need to improve performance on knowledge-intensive NLP tasks that require the model to generate answers based on evidence from retrieved passages. ### When should I choose LLMSurvey over evidentiality_qa? Choose LLMSurvey over evidentiality_qa 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: chain-of-thought, chatgpt, in-context-learning, instruction-tuning; Also covers LLM Frameworks; 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 avoid evidentiality_qa? Avoid using evidentiality_qa if your task does not involve knowledge-intensive NLP tasks that require evidence-based reasoning. Do not use evidentiality_qa if you lack the necessary silver evidentiality data for training, as the model relies on this data to predict evidentiality and generate answers. ### 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. ### Is evidentiality_qa or LLMSurvey more popular on GitHub? LLMSurvey has more GitHub stars (12,216 vs 44). Stars measure visibility, not whether either tool fits your constraints. ### Are evidentiality_qa and LLMSurvey open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to evidentiality_qa or LLMSurvey? GraphCanon lists graph-backed alternatives at [evidentiality_qa alternatives](/tools/akariasai-evidentiality-qa/alternatives) and [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) ([evidentiality_qa markdown twin](/tools/akariasai-evidentiality-qa/alternatives.md), [LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/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/akariasai-evidentiality-qa-vs-rucaibox-llmsurvey.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, evidentiality_qa or LLMSurvey? evidentiality_qa: Dormant. LLMSurvey: 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 evidentiality_qa and LLMSurvey? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [evidentiality_qa trust report](/tools/akariasai-evidentiality-qa/trust); [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=akariasai-evidentiality-qa`](/api/graphcanon/graph?tool=akariasai-evidentiality-qa) - 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/_