Autobool-Qwen4b-Reasoning

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.

• Live Demo: AutoBool on Hugging Face Spaces

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

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:

@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
• Paper: Accepted at EACL 2026

License

Apache 2.0