Comparison
LLMSurvey vs LongCite
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.
Markdown twin · LLMSurvey alternatives · LongCite alternatives
GraphCanon updated 2d
Trust & integrity
| Signal | LLMSurvey | LongCite |
|---|---|---|
| Maintenance | Dormant (523d since push) As of 2d · github_public_v1 | Dormant (570d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2d · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- LLMSurvey
- 12k
- LongCite
- 520
Forks
- LLMSurvey
- 931
- LongCite
- 30
Open issues
- LLMSurvey
- 30
- LongCite
- 9
Language
- LLMSurvey
- Python
- LongCite
- Python
Adopt for
- LLMSurvey
- 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
- LongCite is designed to enhance large language models by enabling them to generate fine-grained citations when answering queries with long context.
Persona
- LLMSurvey
- -
- LongCite
- -
Runtime
- LLMSurvey
- -
- LongCite
- -
License
- LLMSurvey
- The license for LLMSurvey is unknown based on the provided repository information.
- LongCite
- Apache-2.0
Last pushed
- LLMSurvey
- Mar 11, 2025
- LongCite
- Dec 31, 2024
Categories
- LLMSurvey
- Evaluation & Observability, LLM Frameworks
- LongCite
- Evaluation & Observability, LLM Frameworks
Trust and health
Days since push
- LLMSurvey
- 523d
- LongCite
- 570d
Open issues (now)
- LLMSurvey
- 30
- LongCite
- 9
Stars delta
- LLMSurvey
- +18 (30d)
- LongCite
- Unknown
Open issues delta
- LLMSurvey
- 0 (30d)
- LongCite
- Unknown
Full report
- LLMSurvey
- Trust report
- LongCite
- Trust report
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.
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (RUCAIBox/LLMSurvey) · observed Aug 17, 2026
- GitHub forks (RUCAIBox/LLMSurvey) · observed Aug 17, 2026
- Last push (RUCAIBox/LLMSurvey) · observed Mar 11, 2025
- License file (unknown) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (THUDM/LongCite) · observed Jul 25, 2026
- GitHub forks (THUDM/LongCite) · observed Jul 25, 2026
- Last push (THUDM/LongCite) · observed Dec 31, 2024
- License file (Apache-2.0) · observed Jul 25, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LLMSurvey 12k · LongCite 520 (synced Aug 17, 2026).
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 520). 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 and LongCite alternatives (LLMSurvey markdown twin, LongCite markdown twin), 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 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; LongCite trust report.