README.md

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

# AutoBool-Qwen4b-Reasoning-objective

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 the **objective method** grounded in domain expertise and structured logic. The model simulates a relevant article and extracts key terms to construct the Boolean query, following a systematic 6-step process.

- **Base Model:** Qwen/Qwen3-4B
- **Training Method:** GRPO (Group Relative Policy Optimization) with LoRA fine-tuning
- **Prompt Strategy:** Objective method (hypothetical article simulation)
  - **Step 1:** Simulate a concise title and abstract (2-3 sentences) of a relevant and focused article
  - **Step 2:** Identify key informative terms or phrases from the simulated text
  - **Step 3:** Categorize each term into: (A) Health conditions/populations, (B) Treatments/interventions, (C) Study designs, or (N/A)
  - **Step 4-5:** Build Boolean query using categorized terms with appropriate field tags and wildcards
  - **Step 6:** Combine all category blocks using AND
  - Output format: `<think>[Simulated abstract + term extraction + categorization + query construction]</think><answer>[Boolean query]</answer>`
- **Domain:** Biomedical literature search (PubMed)
- **Task:** Boolean query generation for high-recall retrieval

## 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
- **Learning Approach:** Example-based pattern recognition

## 🚀 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)


## 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 benefiting from example-guided generation

## How to Use

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

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

# Define your systematic review topic
topic = "Imaging modalities for characterising focal pancreatic lesions"

# 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 step by step as a reasoning process within <think> </think>, and provide the Boolean query formulated <answer> </answer>."},
    {"role": "user", "content": f'You are given a systematic review research topic, with the topic title "{topic}".
You need to simulate a Boolean query construction process using the **objective method**, which is grounded in domain expertise and structured logic.

**Step 1**: Simulate a concise title and abstract (2–3 sentences) of a *relevant and focused* article clearly aligned with the topic. This is a hypothetical but plausible example.

**Step 2**: Based on the simulated text, identify *key informative terms or phrases* that best represent the article's core concepts. Prioritise specificity and informativeness. Avoid overly broad or ambiguous terms.

**Step 3**: Categorise each term into one of the following:
- (A) Health conditions or populations (e.g., diabetes, adolescents)
- (B) Treatments, interventions, or exposures (e.g., insulin therapy, air pollution)
- (C) Study designs or methodologies (e.g., randomized controlled trial, cohort study)
- (N/A) Not applicable to any of the above categories

**Step 4**: Using the categorised terms, build a Boolean query in MEDLINE format for PubMed:
- Combine synonyms or related terms within each category using OR
- 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)
- Tag each term individually when needed (e.g., covid-19[ti] vaccine[ti] children[ti])
- Field tags limit the search to specific fields and disable ATM

**Step 5**: 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]).

**Step 6**: Combine all category blocks using AND:
((itemA1[tiab] OR itemA2[tiab] OR itemA3[mh]) AND (itemB1[tiab] OR ...) AND (itemC1[tiab] OR ...))

**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]

Place your full reasoning (including simulated abstract, term list, classification, and query construction) inside <think></think>.
Output the final Boolean query inside <answer></answer>.
Do not include anything outside the <think> and <answer> tags.
Do not include date restrictions.'}
]

# 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("Objective method reasoning (simulated article + term extraction):", reasoning)
    print("
Query:", query)
```

## Advantages

- Simulates real-world article abstracts to ground query construction
- Systematic categorization of terms (Health conditions, Interventions, Study designs)
- Grounded in domain expertise and structured logic
- May identify more relevant and specific search terms through hypothetical article simulation

## 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