Comparison
LLMSys-PaperList vs gpl
Verdict
Pick LLMSys-PaperList if lLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems; pick gpl if gPL enhances dense retrieval models by adapting them to new domains without the need for labeled data, relying solely on unlabeled corpora.
Markdown twin · LLMSys-PaperList alternatives · gpl alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | LLMSys-PaperList | gpl |
|---|---|---|
| Maintenance | Active (12d since push) As of 2w · github_public_v1 | Dormant (1144d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 1d · 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
- LLMSys-PaperList
- Curated list of academic papers related to Large Language Model systems
- gpl
- Unsupervised domain adaptation method for dense retrieval using generative pseudo labeling
Stars
- LLMSys-PaperList
- 2.2k
- gpl
- 342
Forks
- LLMSys-PaperList
- 120
- gpl
- 38
Open issues
- LLMSys-PaperList
- 1
- gpl
- 26
Language
- LLMSys-PaperList
- Python
- gpl
- Python
Adopt for
- LLMSys-PaperList
- LLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems.
- gpl
- GPL enhances dense retrieval models by adapting them to new domains without the need for labeled data, relying solely on unlabeled corpora.
Persona
- LLMSys-PaperList
- -
- gpl
- -
Runtime
- LLMSys-PaperList
- -
- gpl
- -
License
- LLMSys-PaperList
- (unknown)
- gpl
- Apache-2.0
Last pushed
- LLMSys-PaperList
- Jul 25, 2026
- gpl
- Jul 6, 2023
Categories
- LLMSys-PaperList
- Inference & Serving, LLM Frameworks, Model Training
- gpl
- Data & Retrieval, Model Training
Trust and health
Maintenance
- LLMSys-PaperList
- Active (82%)
- gpl
- Dormant (18%)
Days since push
- LLMSys-PaperList
- 12d
- gpl
- 1144d
Open issues (now)
- LLMSys-PaperList
- 1
- gpl
- 26
Stars delta
- LLMSys-PaperList
- Unknown
- gpl
- -1 (30d)
Open issues delta
- LLMSys-PaperList
- Unknown
- gpl
- 0 (30d)
Owner type
- LLMSys-PaperList
- User
- gpl
- Organization
Full report
- LLMSys-PaperList
- Trust report
- gpl
- Trust report
Choose LLMSys-PaperList if…
- (repository does not specify hosting environment)
- Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers.
- Also covers Inference & Serving, LLM Frameworks.
- - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems.
When NOT to use LLMSys-PaperList
- - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models.
- - When your primary need is documentation or code examples rather than academic papers and project insights.
- - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ
Choose gpl if…
- Tags unique to gpl: bert, domain-adaptation, information-retrieval, nlp.
- Also covers Data & Retrieval.
- When you have an abundance of unlabeled data from a target domain but lack labeled data.
When NOT to use gpl
- Avoid when high precision and recall on labeled datasets are critical in the initial phase without adaptation.
- If significant computational resources for unsupervised learning are not available, then GPL may not be suitable.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (AmberLJC/LLMSys-PaperList) · observed Aug 6, 2026
- GitHub forks (AmberLJC/LLMSys-PaperList) · observed Aug 6, 2026
- Last push (AmberLJC/LLMSys-PaperList) · observed Jul 25, 2026
- License file (unknown) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (UKPLab/gpl) · observed Aug 23, 2026
- GitHub forks (UKPLab/gpl) · observed Aug 23, 2026
- Last push (UKPLab/gpl) · observed Jul 6, 2023
- License file (Apache-2.0) · observed Aug 23, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LLMSys-PaperList 2.2k · gpl 342 (synced Aug 6, 2026).
Common questions
- What is the difference between LLMSys-PaperList and gpl?
- LLMSys-PaperList: Curated list of academic papers related to Large Language Model systems. gpl: Unsupervised domain adaptation method for dense retrieval using generative pseudo labeling. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLMSys-PaperList over gpl?
- Choose LLMSys-PaperList over gpl when (repository does not specify hosting environment); Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers; Also covers Inference & Serving, LLM Frameworks; - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems.
- When should I choose gpl over LLMSys-PaperList?
- Choose gpl over LLMSys-PaperList when Tags unique to gpl: bert, domain-adaptation, information-retrieval, nlp; Also covers Data & Retrieval; When you have an abundance of unlabeled data from a target domain but lack labeled data.
- When should I avoid LLMSys-PaperList?
- - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models. - When your primary need is documentation or code examples rather than academic papers and project insights. - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ
- When should I avoid gpl?
- Avoid when high precision and recall on labeled datasets are critical in the initial phase without adaptation. If significant computational resources for unsupervised learning are not available, then GPL may not be suitable.
- Is LLMSys-PaperList or gpl more popular on GitHub?
- LLMSys-PaperList has more GitHub stars (2,220 vs 342). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMSys-PaperList and gpl open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to LLMSys-PaperList or gpl?
- GraphCanon lists graph-backed alternatives at LLMSys-PaperList alternatives and gpl alternatives (LLMSys-PaperList markdown twin, gpl 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, LLMSys-PaperList or gpl?
- LLMSys-PaperList: Active. gpl: 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 LLMSys-PaperList and gpl?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMSys-PaperList trust report; gpl trust report.