README.md

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

# AutoBool-Qwen4b-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 **explicit chain-of-thought reasoning**. The model is instructed to provide detailed reasoning about the query construction process inside `<think></think>` tags before generating the final Boolean query.

- **Base Model:** Qwen/Qwen2.5-4B
- **Training Method:** GRPO (Group Relative Policy Optimization) with LoRA fine-tuning
- **Prompt Strategy:** Chain-of-thought reasoning
  - System instruction: "Your reasoning process should be enclosed within `<think></think>`, and the final Boolean query must be enclosed within `<answer></answer>` tags"
  - Output format: `<think>[Detailed step-by-step reasoning explaining the query construction process]</think><answer>[Boolean query]</answer>`
  - Provides full explanation of term selection, MeSH terms, field tags, wildcards, and Boolean logic
- **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
- **Reasoning Approach:** Explicit thinking process with structured tags

## 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
- Applications where reasoning transparency is valuable

## How to Use

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import re

model_name = "ielabgroup/Autobool-Qwen4b-Reasoning"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define your systematic review topic
topic = "Diagnostic accuracy of endoscopic ultrasonography (EUS) for the preoperative locoregional staging of primary gastric cancer"

# Construct the prompt with system and user messages
messages = [
    {"role": "system", "content": "You are an expert systematic review information specialist.\nYou are tasked to formulate a systematic review Boolean query in response to a research topic.\nYour reasoning process should be enclosed within <think></think>, and the final Boolean query must be enclosed within <answer></answer> tags. Do not include anything outside of these tags."},
    {"role": "user", "content": f'You are given a systematic review research topic, with the topic title "{topic}".\nYour task is to generate a highly effective Boolean query in MEDLINE format for PubMed.\nThe query should balance **high recall** (capturing all relevant studies) with **reasonable precision** (avoiding irrelevant results):\n- Use both free-text terms and MeSH terms (e.g., chronic pain[tiab], Pain[mh]).\n- **Do not wrap terms or phrases in double quotes**, as this disables automatic term mapping (ATM).\n- Combine synonyms or related terms within a concept using OR.\n- Combine different concepts using AND.\n- Use wildcards (*) to capture word variants (e.g., vaccin* → vaccine, vaccination):\n  - Terms must have ≥4 characters before the * (e.g., colo*)\n  - Wildcards work with field tags (e.g., breastfeed*[tiab]).\n- Field tags limit the search to specific fields and disable ATM.\n- Do not include date limits.\n- Tag terms using appropriate fields (e.g., covid-19[ti] vaccine[ti] children[ti]) when needed.\n**Only use the following allowed field tags:**\nTitle: [ti], Abstract: [ab], Title/Abstract: [tiab]\nMeSH: [mh], Major MeSH: [majr], Supplementary Concept: [nm]\nText Words: [tw], All Fields: [all]\nPublication Type: [pt], Language: [la]\n\nOutput your full reasoning inside <think></think>.\nOutput the final Boolean query inside <answer></answer>.\nDo not include any content outside these 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=4096)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

# Extract reasoning and query
reasoning_match = re.search(r'<think>(.*?)</think>', response, re.DOTALL)
query_match = re.search(r'<answer>(.*?)</answer>', response, re.DOTALL)

if reasoning_match and query_match:
    reasoning = reasoning_match.group(1).strip()
    query = query_match.group(1).strip()
    print("Reasoning:", reasoning)
    print("\nQuery:", query)
```

The model will generate output with reasoning:
```
<think>
[Detailed step-by-step reasoning explaining the query construction process,
including term selection, MeSH terms, field tags, wildcards, and Boolean logic]
</think>
<answer>
[Final Boolean query]
</answer>
```

## Advantages

- Provides interpretable reasoning process
- Can help understand query construction decisions
- May improve query quality through structured thinking

## 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={2026}
}
```

## More Information

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

## License

Apache 2.0