README.md

---
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- academic-paper-review
- llama-factory
- sft
- iclr-trained
- qwen2.5
language:
- en
---

# PaperLens-7B-Text-OpenReview-ICLR

SFT fine-tune of **[Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/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 [PaperLens orchestrator](https://github.com/zlab-princeton/PaperLens) (paperprep + scoring server + web UI all wired up). Clone, run setup, and chain into the UI:

```bash
git clone https://github.com/zlab-princeton/PaperLens.git
cd PaperLens
uv tool install .              # installs the `paperlens` CLI globally on PATH
paperlens 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):

```bash
# 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):

```bash
paperlens run /abs/path/to/anonymized.pdf
```

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

```bash
vllm serve skonan/PaperLens-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, calibrated)

Evaluated on `iclr-balanced-test`. Calibration threshold picked on `iclr-balanced-val`. Score = `logprob(Accept) − logprob(Reject)` at the decision-token position. `pA` = predicted accept rate; `A_rec` / `R_rec` = accept / reject recall.

| n_test | Acc | AUC | pA | A_rec | R_rec |
|---|---|---|---|---|---|
| 2495 | 67.0% | 0.650 | 49% | 66.0% | 67.9% |

## 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 PaperLens 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 PaperLens models)
```text
You are an expert academic reviewer tasked with evaluating research papers.
```

### ICLR-trained USER preamble (verbatim)
```text
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)
```text
Outcome: \boxed{Accept}
```
or
```text
Outcome: \boxed{Reject}
```
At inference, the decision logprobs at the boxed-token position (5th generated token under the `qwen` template) are used for calibration; either parse the text or read logprobs directly.

## Concrete example (TEXT, ARXIV-trained)

```text
[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 PaperLens collection

All 8 single-domain SFT models (this one plus 7 siblings) plus the companion **PaperLens-Text** and **PaperLens-Vision** datasets live in the [PaperLens collection](https://huggingface.co/collections/skonan/paperlens-6a0c79da423c3a436b7f6b1a). Pairwise comparisons across {3B, 7B} × {text, vision} × {arxiv, openreview-iclr} are intended.