Methodology & scope
How papers get into Awesome Generative Models, how they are ranked, and what this collection does not cover.
What is in the collection
Awesome Generative Models currently indexes 20,505 papers. Papers are harvested from arXiv and Hugging Face Papers, with metadata and citation counts from OpenAlex and Semantic Scholar.
What is not in it
Coverage is recent, not historical. These collections were built by harvesting forward from the date each one started, so foundational papers published before that point are generally absent. On the LLM collection, for example, more than 99% of indexed papers are from 2023 onward, and landmark earlier work — the original Transformer paper, BERT, GPT-3 — is not in the index. Use this as a guide to current work, not as a complete literature record.
Papers are admitted by a per-collection classifier with a published confidence threshold. It is tuned to favour precision, so borderline and cross-listed papers are excluded rather than admitted. Some relevant work will be missing as a result.
What “community signal” means
Ranking uses a heat score: a weighted blend of six signals, normalised on a log scale and decayed by paper age. It measures attention, not quality or correctness.
| Signal | Weight | Notes |
|---|---|---|
| Citations | 30% | OpenAlex citation counts, refreshed on a rolling schedule. |
| GitHub stars | 20% | Stars on repositories linked from the paper. |
| Hugging Face likes | 15% | Likes on an associated Hugging Face paper page. |
| 15% | Currently contributes NOTHING. The upstream feed returns 403, so this term is zero for every paper. It is left in the formula so existing scores stay comparable. | |
| Bluesky | 10% | Mentions found on Bluesky. |
| Hacker News | 10% | Points on a Hacker News submission. |
Citations
Citation counts come from OpenAlex and are a snapshot, not a live figure. They are incomplete: a substantial share of indexed papers have no citation count at all, either because the paper is too recent or because it has no OpenAlex record. A missing count is shown as blank rather than as zero, because those are different things.
What is generated by a model
- Topic tags. Assigned by a classifier, refined by GPT where available; falls back to heuristic tags when it is not.
- Off-topic filtering. A GPT judge decides borderline in-scope/out-of-scope calls; a heuristic filter handles the rest.
- Daily digest & weekly long-form. Written by an LLM from the abstracts and metadata shown on the site. Every claim links to its source paper, and citations that no retrieval returned are stripped before sending.
- Learn curriculum. Concept ordering and resource selection are LLM-assisted, grounded in the indexed corpus.
- Research assistant. Answers are generated by an LLM restricted to the papers it retrieves from this corpus.
Paper titles, authors, abstracts, years and citation counts are taken from the source record and are not model-generated.
Leaderboards
Benchmark tables are fetched from the benchmark’s own publisher where one exists, and each board records its source. Numbers self-reported in papers are labelled as such and are excluded from the default ranking unless they state the same evaluation protocol as the board. Where a benchmark stopped publishing machine-readable results, the board is retired rather than left to go stale silently.
Corrections
If something here is wrong, it is a bug and worth reporting. This page describes what the system actually does; where the two disagree, the code is the authority and this page needs fixing.