---
license: apache-2.0
base_model: Qwen/Qwen3-4B
tags:
- boolean-queries
- systematic-review
- information-retrieval
- pubmed
- reinforcement-learning
- grpo
library_name: transformers
---

# AutoBool-Qwen4b-No-reasoning

This model is part of the **AutoBool** framework, a reinforcement learning approach for training large language models to generate high-quality Boolean queries for systematic literature reviews.

## Model Description

This variant uses **direct generation** without explicit reasoning steps. The model is instructed to output only the final Boolean query inside `<answer></answer>` tags without any explanation or reasoning process.

- **Base Model:** Qwen/Qwen3-4B
- **Training Method:** GRPO (Group Relative Policy Optimization) with LoRA fine-tuning
- **Prompt Strategy:** Direct generation (no reasoning)
  - System instruction: "Do not include any explanation or reasoning"
  - Output format: `<answer>[Boolean query]</answer>`
  - No intermediate thinking or explanation steps
- **Domain:** Biomedical literature search (PubMed)
- **Task:** Boolean query generation for high-recall retrieval

## 🚀 Interactive Demo

Try out our query generation models directly in your browser! The demo allows you to test our different reasoning strategies (Standard, Conceptual, Objective, and No-Reasoning) in real-time.

[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/wshuai190/AutoBool-Demo) 
* **Live Demo:** [AutoBool on Hugging Face Spaces](https://huggingface.co/spaces/wshuai190/AutoBool-Demo)


## Training Details

The model was trained using:
- **Optimization:** GRPO (Group Relative Policy Optimization)
- **Fine-tuning:** LoRA (Low-Rank Adaptation)
- **Dataset:** wshuai190/pubmed-pmc-sr-filtered
- **Reward Function:** Combines syntactic validity, format correctness, and retrieval effectiveness

## Intended Use

This model is designed for:
- Generating Boolean queries for systematic literature reviews
- High-recall biomedical information retrieval
- Supporting evidence synthesis in healthcare and biomedical research

## How to Use

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "ielabgroup/Autobool-Qwen4b-No-reasoning"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define your systematic review topic
topic = "Thromboelastography (TEG) and rotational thromboelastometry (ROTEM) for trauma-induced coagulopathy"

# Construct the prompt with system and user messages
messages = [
    {"role": "system", "content": "You are an expert systematic review information specialist.
You are tasked to formulate a systematic review Boolean query in response to a research topic. The final Boolean query must be enclosed within <answer> </answer> tags. Do not include any explanation or reasoning."},
    {"role": "user", "content": f'You are given a systematic review research topic, with the topic title "{topic}".
Your task is to formulate a highly effective Boolean query in MEDLINE format for PubMed.
The query should balance **high recall** (capturing all relevant studies) with **reasonable precision** (avoiding irrelevant results):
- Use both free-text terms and MeSH terms (e.g., chronic pain[tiab], Pain[mh]).
- **Do not wrap terms or phrases in double quotes**, as this disables automatic term mapping (ATM).
- Combine synonyms or related terms within a concept using OR.
- Combine different concepts using AND.
- Use wildcards (*) to capture word variants (e.g., vaccin* → vaccine, vaccination):
  - Terms must have ≥4 characters before the * (e.g., colo*)
  - Wildcards work with field tags (e.g., breastfeed*[tiab]).
- Field tags limit the search to specific fields and disable ATM.
- Do not include date limits.
- Tag term using term field (e.g., covid-19[ti] vaccine[ti] children[ti]) when needed.
**Only use the following allowed field tags:**
Title: [ti], Abstract: [ab], Title/Abstract: [tiab]
MeSH: [mh], Major MeSH: [majr], Supplementary Concept: [nm]
Text Words: [tw], All Fields: [all]
Publication Type: [pt], Language: [la]

Output and only output the formulated Boolean query inside <answer></answer> tags. Do not include any explanation or content outside or inside the <answer> tags.'}
]

# Generate the query
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=2048)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

# Extract the query from <answer> tags
import re
match = re.search(r'<answer>(.*?)</answer>', response, re.DOTALL)
if match:
    query = match.group(1).strip()
    print(query)
```

## Limitations

- Optimized specifically for PubMed Boolean query syntax
- Performance may vary on non-biomedical domains
- Requires domain knowledge for effective prompt engineering

## Citation

If you use this model, please cite:

```bibtex
@inproceedings{autobool2026,
  title={AutoBool: Reinforcement Learning for Boolean Query Generation in Systematic Reviews},
  author={[Shuai Wang, Harrisen Scells, Bevan Koopman, Guido Zuccon]},
  booktitle={Proceedings of the 2026 Conference of the European Chapter of the Association for Computational Linguistics (EACL)},
  year={2025}
}
```

## More Information

- **GitHub Repository:** [https://github.com/ielab/AutoBool](https://github.com/ielab/AutoBool)
- **Paper:** Accepted at EACL 2026

## License

Apache 2.0