---
license: apache-2.0
language:
- en
tags:
- peer-review
- scientific-review
- lora
- qwen
- fine-tuned
- icml2026
pipeline_tag: text-generation
---

# FirstPass: Grounding AI Scientific Judgment in Multi-Round Editorial Outcomes

Official model weights for the **ICML 2026 AI4Science Workshop** papers:
- **Research Track**: "FirstPass: Grounding AI Scientific Judgment in Multi-Round Editorial Outcomes"
- **Dataset Competition Track**: "FIRSTPASS: A Multi-Domain, Multi-Round Peer Review Dataset Grounded in Real Editorial Outcomes"

**Authors:** Prabhjot Singh, Somnath Luitel, Manmeet Singh, Josh Durkee

---

## 🗂️ What's in this repo

| Folder | Task | Description |
|---|---|---|
| `cls_revision_prediction/` | Classification | Predicts Standard (2-round) vs Extended (3+ round) editorial outcome |
| `sft_review_generation/` | Generation | Generates full scientific peer reviews |
| `checkpoints_v4/cls_adapter/final/` | Classification (alt path) | Same cls adapter via checkpoints folder |
| `checkpoints_v4/sft_adapter/final/` | Generation (alt path) | Same sft adapter via checkpoints folder |

**Use `cls_revision_prediction/` and `sft_review_generation/` directly** — those are the clean final adapters.

---

## 📊 Key Results

| Task | Metric | Score |
|---|---|---|
| Revision-Cycle Prediction | Accuracy | **80.5%** |
| Revision-Cycle Prediction | F1-macro | **78.2%** |
| Review Generation | ROUGE-L | **0.154** |
| Review Generation | Avg. length | 1,187 words |

The masking finding: without response-only loss masking → 62.0% accuracy (below majority baseline). With masking → 80.5%. For long-input/short-output classification, masking is an architectural prerequisite, not an optimization trick.

---

## 🚀 Quick Start

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = "Qwen/Qwen2.5-7B-Instruct"  # or whichever base was used
adapter_path = "prabhjotschugh/FirstPass-Models/cls_revision_prediction"

tokenizer = AutoTokenizer.from_pretrained(adapter_path)
model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, adapter_path)
```

---

## 📥 Dataset

[🤗 firstpass-peer-review](https://huggingface.co/datasets/Prabhjotschugh/firstpass-peer-review) — 3,668 multi-round peer-review dialogues from *Nature Communications* across 5 domains (biology, chemistry, neuroscience, physics, earth science).