Comparison
Awesome-LLMs-ICLR-24 vs LongCite
Verdict
Pick Awesome-LLMs-ICLR-24 if awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024; 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 · Awesome-LLMs-ICLR-24 alternatives · LongCite alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-LLMs-ICLR-24 | LongCite |
|---|---|---|
| Maintenance | Dormant (856d since push) As of 2w · github_public_v1 | Dormant (570d since push) As of 4w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 4w · 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
- Awesome-LLMs-ICLR-24
- Compilation of LLM papers from ICLR 2024
- LongCite
- Enabling LLMs to Generate Fine-grained Citations in Long-context QA
Stars
- Awesome-LLMs-ICLR-24
- 72
- LongCite
- 520
Forks
- Awesome-LLMs-ICLR-24
- 5
- LongCite
- 30
Open issues
- Awesome-LLMs-ICLR-24
- 0
- LongCite
- 9
Language
- Awesome-LLMs-ICLR-24
- -
- LongCite
- Python
Adopt for
- Awesome-LLMs-ICLR-24
- Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024.
- LongCite
- LongCite is designed to enhance large language models by enabling them to generate fine-grained citations when answering queries with long context.
Persona
- Awesome-LLMs-ICLR-24
- -
- LongCite
- -
Runtime
- Awesome-LLMs-ICLR-24
- -
- LongCite
- -
License
- Awesome-LLMs-ICLR-24
- MIT
- LongCite
- Apache-2.0
Last pushed
- Awesome-LLMs-ICLR-24
- Apr 4, 2024
- LongCite
- Dec 31, 2024
Categories
- Awesome-LLMs-ICLR-24
- Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
- LongCite
- Evaluation & Observability, LLM Frameworks
Trust and health
Days since push
- Awesome-LLMs-ICLR-24
- 856d
- LongCite
- 570d
Open issues (now)
- Awesome-LLMs-ICLR-24
- 0
- LongCite
- 9
Owner type
- Awesome-LLMs-ICLR-24
- User
- LongCite
- Organization
Full report
- Awesome-LLMs-ICLR-24
- Trust report
- LongCite
- Trust report
Choose Awesome-LLMs-ICLR-24 if…
- License: Awesome-LLMs-ICLR-24 is MIT, LongCite is Apache-2.0.
- Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework.
- Also covers Developer Tools, Inference & Serving, Model Training.
- If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights.
When NOT to use Awesome-LLMs-ICLR-24
- If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024.
- For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis.
Choose LongCite if…
- License: LongCite is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT.
- 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.
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 (azminewasi/Awesome-LLMs-ICLR-24) · observed Aug 8, 2026
- GitHub forks (azminewasi/Awesome-LLMs-ICLR-24) · observed Aug 8, 2026
- Last push (azminewasi/Awesome-LLMs-ICLR-24) · observed Apr 4, 2024
- License file (MIT) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 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: Awesome-LLMs-ICLR-24 72 · LongCite 520 (synced Aug 8, 2026).
Common questions
- What is the difference between Awesome-LLMs-ICLR-24 and LongCite?
- Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. 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 Awesome-LLMs-ICLR-24 over LongCite?
- Choose Awesome-LLMs-ICLR-24 over LongCite when License: Awesome-LLMs-ICLR-24 is MIT, LongCite is Apache-2.0; Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Inference & Serving, Model Training; If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights.
- When should I choose LongCite over Awesome-LLMs-ICLR-24?
- Choose LongCite over Awesome-LLMs-ICLR-24 when License: LongCite is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT; 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.
- When should I avoid Awesome-LLMs-ICLR-24?
- If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis.
- 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 Awesome-LLMs-ICLR-24 or LongCite more popular on GitHub?
- LongCite has more GitHub stars (520 vs 72). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLMs-ICLR-24 and LongCite open source?
- Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, LongCite: Apache-2.0).
- Where can I find alternatives to Awesome-LLMs-ICLR-24 or LongCite?
- GraphCanon lists graph-backed alternatives at Awesome-LLMs-ICLR-24 alternatives and LongCite alternatives (Awesome-LLMs-ICLR-24 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, Awesome-LLMs-ICLR-24 or LongCite?
- Awesome-LLMs-ICLR-24: 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 Awesome-LLMs-ICLR-24 and LongCite?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLMs-ICLR-24 trust report; LongCite trust report.