---
license: cc-by-4.0
task_categories: [text-ranking, text-retrieval]
language: [en]
tags: [benchmark, reranker, hard-negatives, peer-review, scientific-documents, information-retrieval]
pretty_name: 44b Eval
size_categories: [1K<n<10K]
---

<div align="left">

# 44b Eval

*A frozen 9-way reranking benchmark over machine-learning papers, queried by their peer reviews.*

[**44b Collection**](https://huggingface.co/collections/NYSgpt/44b-6a83f41db09da3efaf5868f8) | [**44b-reranker**](https://huggingface.co/NYSgpt/44b-reranker) | [**44b-reranker-mini**](https://huggingface.co/NYSgpt/44b-reranker-mini)

</div>

The benchmark behind every number on the 44b reranker cards: which review is the query,
which paper is the gold, which eight papers are the hard negatives, and the scored result of
every arm — so anyone can check a published number or score their own reranker on the
same 3,157 queries.

- ✅ **Paper-disjoint from training.** None of the 1,534 test papers appears in training
  as a positive; 23 (1.5%) appear as some other query's negative, which biases against
  the fine-tuned models rather than for them.
- ✅ **Real expert queries.** Each query is a peer reviewer's own summary of a paper,
  written by a domain expert on OpenReview.
- ✅ **Hard by construction.** 8 negatives per query mined from embedding-space nearest
  neighbours and lexical near-matches, screened for duplicates. Chance P@1 = 0.111.
- ✅ **Keys-only.** OpenReview note IDs and arXiv / OpenAlex / Semantic Scholar paper
  IDs, each with its public URL — no review text, no abstracts.

---
## Contents

| file | rows | what |
|---|---:|---|
| `test.jsonl` | 3,157 | `{note_id, note_url, kind, positive{scheme,id,url}, negatives[8]{scheme,id,url}}` — one line per query |
| `results.jsonl` | 7 | the scorer's output per arm: P@1, P@2, P@3, MRR, mean rank, ms/query, truncation settings |
| `manifest.json` | 1 | split rule and seed, counts, disjointness proof, negative-mining recipe, ID-scheme census |

**Rebuilding the text.** The query is the `summary` field of an `official_review` (or the
`metareview` of a `meta_review`) at `note_url`. Each candidate is the title + abstract of
the paper at its `url`: `arxiv` IDs resolve at arxiv.org, `openalex` IDs at openalex.org,
`s2` IDs at semanticscholar.org. 23 negatives (0.1%) are curated web documents with no
third-party identifier and are marked `resolvable: false`.

---
## Performance

3,157 queries over 1,534 papers, 9 candidates each (1 positive + 8 hard negatives).
Model arms on one A10G; retrieval arms are first-stage rankings of the same 9-candidate
sets.

| Rank | Arm | P@1 | P@3 | MRR | mean rank | ms/query |
|---:|---|---:|---:|---:|---:|---:|
| **1** | [**44b-reranker**](https://huggingface.co/NYSgpt/44b-reranker) (149M, fine-tuned) | **0.929** | **0.982** | **0.956** | **1.148** | 41.1 |
| 2 | [**44b-reranker-mini**](https://huggingface.co/NYSgpt/44b-reranker-mini) (22.7M, fine-tuned) | 0.865 | 0.954 | 0.914 | 1.322 | **7.4** |
| 3 | BM25 (keyword) | 0.856 | 0.940 | 0.905 | 1.407 | — |
| 4 | `gte-reranker-modernbert-base` (off the shelf) | 0.833 | 0.926 | 0.889 | 1.464 | 53.1 |
| 5 | Hybrid RRF (k = 60) | 0.728 | 0.793 | 0.788 | 2.371 | — |
| 6 | Dense (`text-embedding-3-small`) | 0.681 | 0.749 | 0.745 | 2.859 | — |
| 7 | `ms-marco-MiniLM-L-6-v2` (off the shelf) | 0.643 | 0.817 | 0.750 | 2.210 | 9.4 |

These are *reranking* numbers — picking the right paper out of nine confusable ones — not
open-corpus retrieval.

---
## Details

| Property | |
|---|---|
| **Corpus** | 44B — arXiv, OpenAlex and Semantic Scholar records unified with OpenReview reviews; ICLR 2024–2025, NeurIPS 2024 |
| **Query** | an official review's `summary`, or a meta-review's `metareview`; ≥ 200 characters |
| **Split** | by paper — `sha256("44b-training-pairs-2026-07-31:" + paper) → [0,1)`, train < 0.8 ≤ val < 0.9 ≤ test |
| **Disjointness** | `|train ∩ val| = |train ∩ test| = |val ∩ test| = 0`, proven at build time |
| **Negatives** | 8 per query: semantic (HNSW over `text-embedding-3-small`, ef_search 160) and lexical (Postgres FTS); false-negative screen on Semantic Scholar ID and near-identical title |
| **Truncation at scoring** | query 600 characters, document 900 characters |
| **Frozen** | 2026-07-31 |

---
## How to use

```python
import json

rows = [json.loads(l) for l in open("test.jsonl")]
for r in rows:
    query_url = r["note_url"]                       # fetch the review's summary here
    candidates = [r["positive"], *r["negatives"]]   # fetch title + abstract at each ["url"]
    # score all 9 with your reranker; the positive is at index 0 before you shuffle
```

---
## Licensing

Review text belongs to its authors on OpenReview; abstracts come from Semantic Scholar,
OpenAlex and arXiv under their own terms. None of that text is redistributed here. This
release ships identifiers, the benchmark construction, and our own measurements — released
**CC-BY-4.0**. A third party resolves the IDs against the same public sources we did, which
is the standard MS MARCO / BEIR shape.

---
## 📬 Contact

Questions, results, or a model to add to the table? Open a discussion in the
[Community tab](https://huggingface.co/datasets/NYSgpt/44b-eval/discussions).

## Citation

```bibtex
@misc{44beval2026,
  title  = {44b Eval: a peer-review-queried hard-negative reranking benchmark over ML papers},
  author = {NYSgpt},
  year   = {2026},
  url    = {https://huggingface.co/datasets/NYSgpt/44b-eval}
}
```