Update README.md

README.md

@@ -64,15 +64,36 @@ model = AutoModelForCausalLM.from_pretrained(
 inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 outputs = model.generate(**inputs, max_new_tokens=1200)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
 
+## Intended Use
 
-# Citation
-### If you use this model or method in your research, please cite our paper:
+Struct-SQL-4B is intended for **research and academic use** in tasks involving **Text-to-SQL generation** and **semantic parsing over relational databases**. The model is particularly suited for studying:
 
+- Knowledge distillation techniques that leverage **structured intermediate representations**
+- Explicit **query planning** as an alternative to unstructured chain-of-thought reasoning
+- Error reduction in SQL generation, including syntactic validity and schema grounding
+- Compact language models for complex reasoning under limited parameter budgets
+
+The model is not optimized for direct deployment in production database systems without additional validation and safety constraints.
+
+---
+
+## Limitations
+
+- Evaluation is confined to the SQLite-based BIRD benchmark
+- The model may generate logically plausible but incorrect SQL for highly complex multi-hop queries
+
+---
+
+## Citation
+
+```bibtex
 @article{thaker2025knowledge,
   title={Knowledge Distillation with Structured Chain-of-Thought for Text-to-SQL},
   author={Thaker, Khushboo and Bresler, Yony},
   journal={arXiv preprint arXiv:2512.17053},
   year={2025}
-}
+}
+