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Model: ielabgroup/Autobool-Qwen4b-No-reasoning Source: Original Platform
2026-05-18 07:54:20 +08:00
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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B
+tags:
+- boolean-queries
+- systematic-review
+- information-retrieval
+- pubmed
+- reinforcement-learning
+- grpo
+library_name: transformers
+---
+
+# AutoBool-Qwen4b-No-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 **direct generation** without explicit reasoning steps. The model is instructed to output only the final Boolean query inside `<answer></answer>` tags without any explanation or reasoning process.
+
+- **Base Model:** Qwen/Qwen3-4B
+- **Training Method:** GRPO (Group Relative Policy Optimization) with LoRA fine-tuning
+- **Prompt Strategy:** Direct generation (no reasoning)
+  - System instruction: "Do not include any explanation or reasoning"
+  - Output format: `<answer>[Boolean query]</answer>`
+  - No intermediate thinking or explanation steps
+- **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
+
+## 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
+
+## How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "ielabgroup/Autobool-Qwen4b-No-reasoning"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Define your systematic review topic
+topic = "Thromboelastography (TEG) and rotational thromboelastometry (ROTEM) for trauma-induced coagulopathy"
+
+# 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 in response to a research topic. The final Boolean query must be enclosed within <answer> </answer> tags. Do not include any explanation or reasoning."},
+    {"role": "user", "content": f'You are given a systematic review research topic, with the topic title "{topic}".
+Your task is to formulate a highly effective Boolean query in MEDLINE format for PubMed.
+The query should balance **high recall** (capturing all relevant studies) with **reasonable precision** (avoiding irrelevant results):
+- 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).
+- Combine synonyms or related terms within a concept using OR.
+- Combine different concepts using AND.
+- 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]).
+- Field tags limit the search to specific fields and disable ATM.
+- Do not include date limits.
+- Tag term using term field (e.g., covid-19[ti] vaccine[ti] children[ti]) when needed.
+**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 and only output the formulated Boolean query inside <answer></answer> tags. Do not include any explanation or content outside or inside the <answer> 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=2048)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Extract the query from <answer> tags
+import re
+match = re.search(r'<answer>(.*?)</answer>', response, re.DOTALL)
+if match:
+    query = match.group(1).strip()
+    print(query)
+```
+
+## 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={2025}
+}
+```
+
+## More Information
+
+- **GitHub Repository:** [https://github.com/ielab/AutoBool](https://github.com/ielab/AutoBool)
+- **Paper:** Accepted at EACL 2026
+
+## License
+
+Apache 2.0