PaperScorer-7B-Text-OpenReview-ICLR

SFT fine-tune of Qwen/Qwen2.5-7B-Instruct trained to predict ICLR-style Accept/Reject verdicts on academic papers from the paper text.

• Modality: text
• Training data: iclr-21k (85/5/10 balanced split)
• Checkpoint: step 1322, end of epoch 2 of 4 (7B → ep2 by convention)
• Hyperparams: LR 1e-6, cosine_then_constant scheduler (decay_ratio 0.75, min_lr_rate 0.001), batch size 32, cutoff_len 24480, framework LLaMA-Factory + FSDP2.

Quickstart — serve + submit a PDF or LaTeX source dir

Easiest path uses the PaperScorer orchestrator (paperprep + scoring server + web UI all wired up). Clone, run setup, and chain into the UI:

git clone https://github.com/zlab-princeton/PaperScorer.git
cd PaperScorer
uv tool install .              # installs the `paperscorer` CLI globally on PATH
paperscorer setup --serve        # in the wizard, pick: size=7B · modality=text · domain=iclr
# → web UI on http://localhost:8003 (PDF upload + LaTeX dir browse)

Or hit the API directly (FastAPI on the same port):

# Submit an anonymized PDF; poll for the verdict
JOB=$(curl -s -F file=@anonymized.pdf http://localhost:8003/submit | jq -r .job_id)
curl http://localhost:8003/status/$JOB        # → job dict: state, verdict, p_accept, ...

# Submit a LaTeX source directory (anonymized) or an arXiv id
curl -X POST http://localhost:8003/submit_latex \
     -H "Content-Type: application/json" \
     -d '{"path": "/abs/path/to/anonymized_latex_dir"}'
curl -X POST http://localhost:8003/submit_arxiv \
     -H "Content-Type: application/json" \
     -d '{"arxiv_id": "2511.08364"}'

Headless one-shot (no server):

paperscorer run /abs/path/to/anonymized.pdf

Lower-level: stand up just a vLLM scoring server with pre-prep'd sharegpt rows (skips paperprep):

vllm serve skonan/PaperScorer-7B-Text-OpenReview-ICLR --task generate --gpu-memory-utilization 0.85
# OpenAI-compat API on :8000 — format prompts per the "Prompt format" section below.

Test results (in-distribution)

Evaluated on iclr-balanced-test (2025/26 papers). The prediction is the model's own decision: Accept when the decision-token log-odds ℓ = logprob(Accept) − logprob(Reject) is ≥ 0, which is the same as parsing the \boxed{} answer. No threshold is tuned. pA = predicted accept rate; A_rec / R_rec = accept / reject recall.

n_test	Acc	AUC	pA	A_rec	R_rec
1667	66.0%	0.720	47%	62.8%	69.3%

Calibration (temperature scaling with a base-rate term)

ℓ measures the evidence for Accept under the balanced 50/50 prior the model was trained on. To turn it into a calibrated chance of acceptance at a venue's acceptance rate π, divide by the temperature τ = 1.135 and add the prior log-odds:

p_accept = σ(ℓ / τ + log(π / (1 − π))), with τ = 1.135 and π = 0.306 for this model.

import math
ell = logprob_accept - logprob_reject   # at the decision token; use -50 for a token missing from the top-5

def p_accept(ell, tau=1.135, pi=0.306):
    # pi = the venue's acceptance rate; default is this model's validation acceptance rate
    return 1 / (1 + math.exp(-(ell / tau + math.log(pi / (1 - pi)))))

p_accept(ell)            # chance of acceptance at ICLR's rate (π = 0.306)
p_accept(ell, pi=0.5)    # balanced 50/50 pool: the shift vanishes, giving σ(ℓ / τ)

τ was fit by negative log-likelihood on the natural-acceptance-rate validation split (30.6% accepts), which matches deployment, where most papers are rejected. The base-rate term is inside the fit, with π set to that split's acceptance rate, so the shift handles the base rate and τ only sets how sharp the probabilities are. p_accept is the model's chance of acceptance for the paper at a venue that accepts a fraction π of submissions.

• Decisions are unchanged. The model accepts iff ℓ ≥ 0, which is exactly p_accept ≥ π. Neither τ nor π ever changes a decision, so the test results above hold as reported.
• Other venues. Plug in a different venue's acceptance rate as pi to get the chance of acceptance there, e.g. p_accept(ell, pi=0.25) for a venue that accepts 25% of submissions.
• Balanced pools. On a 50/50 pool, set π = 0.5: the shift vanishes and p_accept = σ(ℓ / τ).

Expected calibration error (10 bins): on iclr-balanced-test (π = 0.5), 0.027 raw → 0.023 calibrated; on the natural-rate ICLR test split, 0.128 without the base-rate term → 0.038 with it. Method and numbers follow the paper's calibration section (Section 5, "Confidence tracks accuracy"; Appendix I.3, "Calibration Details", Table I.1).

Note on training-size asymmetry

ICLR-trained models saw ~21k examples (4 epochs ≈ 2644 / 5296 steps). Arxiv-trained vs ICLR-trained models saw a ~3× data gap — direct comparisons should account for it.

Prompt format

Inputs are sharegpt-style 3-turn conversations: system, human, gpt. SYSTEM is the same string across all 8 PaperScorer models. USER preamble differs per training domain. Vision variants append one <image> token per page-screenshot at the end of the user message.

SYSTEM (all PaperScorer models)

You are an expert academic reviewer tasked with evaluating research papers.

ICLR-trained USER preamble (verbatim)

I am giving you a paper. I want to predict its acceptance outcome at ICLR.
 - Your answer will either be: \boxed{Accept} or \boxed{Reject}
 - Note: ICLR generally has a ~30% acceptance rate

# <PAPER TITLE>
...paper body in markdown...

ASSISTANT (gold)

Outcome: \boxed{Accept}

or

Outcome: \boxed{Reject}

At inference, the decision logprobs at the boxed-token position (5th generated token under the qwen template) give ℓ for temperature scaling (see Calibration above); either parse the text for the decision or read logprobs directly.

Concrete example (TEXT, ARXIV-trained)

[SYSTEM]
You are an expert academic reviewer tasked with evaluating research papers.

[USER]
I am giving you a paper submitted to a top machine-learning venue. Predict its acceptance outcome.
 - Your answer will either be: \boxed{Accept} or \boxed{Reject}
 - Note: typical top-tier ML venues have ~25-30% acceptance rates

# SSAST: SELF-SUPERVISED AUDIO SPECTROGRAM TRANSFORMER

## Abstract
... ~32k chars of paper body ...

[ASSISTANT]
Outcome: \boxed{Accept}

Related models + datasets in the PaperScorer collection

All 8 single-domain SFT models (this one plus 7 siblings) plus the companion PaperScorer-Text and PaperScorer-Vision datasets live in the PaperScorer collection. Pairwise comparisons across {3B, 7B} × {text, vision} × {arxiv, openreview-iclr} are intended.

Citation

If you use PaperScorer, please cite our NeurIPS 2026 paper:

@inproceedings{konan2026paperscorer,
  title     = {PaperScorer: How Predictable Is Paper Acceptance?},
  author    = {Konan, Sachin and Liu, Jonathan and Liu, Zhuang},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2026}
}