--- 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 `` 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 ``, and the final Boolean query must be enclosed within `` tags" - Output format: `[Detailed step-by-step reasoning explaining the query construction process][Boolean query]` - 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 ## 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 , and the final Boolean query must be enclosed within 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 .\nOutput the final Boolean query inside .\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'(.*?)', response, re.DOTALL) query_match = re.search(r'(.*?)', 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: ``` [Detailed step-by-step reasoning explaining the query construction process, including term selection, MeSH terms, field tags, wildcards, and Boolean logic] [Final Boolean query] ``` ## 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