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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-4B
+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/Qwen2.5-4B
+- **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:** PubMed systematic review queries (version 1.2)
+- **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
+
+```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{autobool2025,
+  title={AutoBool: Reinforcement Learning for Boolean Query Generation in Systematic Reviews},
+  author={[]},
+  booktitle={Proceedings of the 2025 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 2025
+
+## License
+
+Apache 2.0