| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| pretty_name: Efficient LLM Papers |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - text-classification |
| - text-generation |
| tags: |
| - arxiv |
| - semantic-scholar |
| - papers |
| - research |
| - machine-learning |
| configs: |
| - config_name: default |
| data_files: data.jsonl |
| --- |
| |
| # Efficient LLM Papers — FineSet |
|
|
| A research-paper dataset on **Efficient LLM Papers**, assembled, deduplicated, and quality-scored by |
| [FineSet](https://fineset.io) from arXiv and Semantic Scholar. |
|
|
| > **📸 This is a dated snapshot — generated 2026-06-12.** |
| > It is not auto-updated. Research on **Efficient LLM Papers** moves fast — new papers land on arXiv every |
| > week. Want this same dataset **refreshed daily**, on a topic *you* choose? See the bottom. ↓ |
|
|
| ## Why this dataset |
|
|
| - **Quality-scored:** `quality_score` float (0–1), citation-normalized — filter out the noise |
| - **Papers with code:** 435 flagged via `has_code` — find reproducible work fast |
| - **Deduplicated:** arXiv + Semantic Scholar cross-referenced, duplicate records merged |
| - **Clean JSONL:** 1734 records, one per line, normalized fields — no encoding garbage |
|
|
| ## Dataset details |
|
|
| - **Records:** 1734 |
| - **Date range:** 2022–2026 |
| - **Snapshot date:** 2026-06-12 (frozen — see note above) |
| - **Sources:** arXiv, Semantic Scholar (cross-referenced, duplicates merged) |
| - **arXiv categories:** cs.LG |
| - **Quality scoring:** citation-normalized, 0–1 (p50=0.15, p90=0.406) |
| - **Format:** JSONL, one record per line |
|
|
| ## Fields |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | id | string | Deterministic SHA256 record id | |
| | sources | list | Which sources contributed (`arxiv`, `semantic_scholar`) | |
| | title | string | Paper title | |
| | abstract | string | Full abstract | |
| | authors | list | Author names | |
| | categories | list | arXiv category codes | |
| | fields_of_study | list | Semantic Scholar field tags | |
| | published_date | string | ISO 8601 date | |
| | url | string | arXiv abstract URL | |
| | pdf_url | string\|null | Open-access PDF if available | |
| | arxiv_id | string\|null | arXiv identifier | |
| | doi | string\|null | DOI if available | |
| | citation_count | int | Citation count (Semantic Scholar) | |
| | influential_citation_count | int | Influential citations (Semantic Scholar) | |
| | has_code | bool | Code repo detected in the arXiv comment | |
| | code_url | string\|null | GitHub URL if detected | |
| | venue | string\|null | Publication venue | |
| | quality_score | float | 0–1, citation-normalized | |
| |
| ## Quality score methodology |
| |
| `quality_score = min(1.0, log10(citation_count + 1) / 4)` |
| |
| A citation-normalized heuristic: 0 for uncited papers, ~0.5 at 100 citations, |
| ~0.75 at 1,000, 1.0 at 10,000+. Useful for filtering training data by impact. |
| |
| ## 👉 Want this on YOUR topic, updated daily? |
| |
| This snapshot is frozen at 2026-06-12. The live FineSet pipeline keeps a dataset like this |
| **refreshed every day** on whatever topic you describe — new papers in, dedup and quality |
| scoring automatic, export as JSONL/Parquet or push straight to the Hub. |
| |
| **Try it now — it's live:** → [fineset.io](https://fineset.io) — describe your research topic |
| in plain English and get a fresh, quality-scored dataset in minutes. Free to start. |
| |
