--- 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-conceptual 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 **conceptual method** for structured query construction. The model follows a systematic 5-step process to identify concepts and build Boolean queries based on domain logic. - **Base Model:** Qwen/Qwen3-4B - **Training Method:** GRPO (Group Relative Policy Optimization) with LoRA fine-tuning - **Prompt Strategy:** Conceptual method (structured 5-step approach) - **Step 1:** Identify 2-3 key concepts from the topic (e.g., Population, Intervention, Outcome) - **Step 2:** For each concept, list related terms (synonyms, variants, relevant MeSH terms) - **Step 3:** Create a Boolean block per concept (combine terms using OR) - **Step 4:** Use wildcards (*) to capture word variants - **Step 5:** Combine all Boolean blocks using AND - Output format: `[Step-by-step conceptual analysis][Boolean query]` - **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:** Structured conceptual decomposition ## 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 requiring systematic concept identification ## How to Use ```python from transformers import AutoTokenizer, AutoModelForCausalLM import re model_name = "ielabgroup/Autobool-Qwen4b-Reasoning-conceptual" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) # Define your systematic review topic topic = "Ultrasonography for diagnosis of alcoholic cirrhosis in people with alcoholic liver disease" # Construct the prompt with system and user messages messages = [ {"role": "system", "content": "You are an expert systematic review information specialist. Formulate a systematic review Boolean query using step-by-step reasoning inside , and output the final query inside ."}, {"role": "user", "content": f'You are given a systematic review topic titled: "{topic}". Construct a Boolean query using the **conceptual method**, based on domain logic and structured thinking. **Step 1**: Identify 2–3 key concepts from the topic (e.g., Population, Intervention, Outcome). **Step 2**: For each concept: - List related terms: synonyms, variants, relevant MeSH terms. - Prioritise specific, high-precision terms. **Step 3**: Create a Boolean block per concept: - Combine terms using OR - Use 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 terms individually when needed (e.g., covid-19[ti] vaccine[ti] children[ti]) - Field tags limit search scope and disable ATM **Step 4**: 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 5**: Combine all Boolean blocks using AND: ((Concept1_term1[tiab] OR Concept1_term2[tiab] OR Concept1_termX[mh]) AND (Concept2_...)) **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] Output your full reasoning inside ... Output only the final Boolean query inside ... Do not include any content outside these tags. Do not include date limits.'} ] # 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("Step-by-step conceptual analysis:", reasoning) print(" Query:", query) ``` ## Advantages - Systematic concept-based approach (Population, Intervention, Outcome framework) - Explicit decomposition of complex topics into manageable concepts - Structured 5-step process ensures comprehensive term coverage - Well-suited for systematic reviews with clear conceptual structure ## 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