Autobool-Qwen4b-Reasoning-objective

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

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

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:

@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
• Paper: Accepted at EACL 2026

License

Apache 2.0