Title: Fine-Tuning LLMs for XQuery to SQL Conversion: Challenges and Opportunities

Introduction

XQuery has emerged as a powerful tool for extracting structured data from various sources, making it essential in fields like healthcare, finance, and scientific research. The ability to convert XQuery into executable SQL queries is critical for leveraging these tools locally without relying on cloud-based solutions. However, this conversion process faces significant hurdles, particularly due to dataset limitations and methodological constraints. A small dataset comprising approximately 100+ samples has hindered the effectiveness of fine-tuning approaches such as parameter-efficient fine-tuning (PEFT) on models like Qwen2.5-Coder 7B. These challenges are compounded by the need for more sophisticated prompting strategies to handle complex or lengthy XQueries effectively.

Challenges

Initial attempts at converting XQuery to SQL using parsing-based methods and prompt-engineering demonstrated promise for straightforward queries but struggled to generalize effectively to longer or more intricate XQueries. This indicates a pressing need for improved prompting strategies that can manage the complexity of structured tasks like SQL translation without compromising speed or code quality. The methodological inefficiencies, such as limited dataset size and inadequate fine-tuning approaches, have constrained the scalability of these methods. Efforts have thus shifted toward exploring alternative approaches, including enhancing prompting techniques, experimenting with structured prompting methods, and investigating different model architectures to improve performance in structured tasks.

Key Specifics

The dataset constraints significantly impact fine-tuning approaches like PEFT on models such as Qwen2.5-Coder 7B, limiting their effectiveness for complex tasks. Initial successes with parsing-based methods and prompt-engineering are promising but scale poorly for longer queries. Alternative methods, such as structured prompting or different model architectures, show promise for improving performance without sacrificing speed or code quality.

Why It Matters

The challenges in converting XQuery to SQL using locally run LLMs directly impact the ability of developers to efficiently process complex SQL translations. Current models may not yet be capable of delivering satisfactory performance for real-world applications requiring scalability and efficiency. This turning point underscores the need for innovative approaches beyond fine-tuning, such as enhanced prompting strategies or new model architectures.

Open Questions

The research brief poses several open questions: whether current models can achieve satisfactory performance with additional data or targeted fine-tuning; the potential effectiveness of integrating structured prompting strategies or exploring alternative architectures; and the robustness of prompting mechanisms across varying query lengths without compromising speed or code quality.

Introduction Expansion

XQuery, as a high-level language for data extraction, plays a pivotal role in enabling organizations to derive insights from diverse data sources efficiently. Its ability to express complex queries concisely makes it indispensable in sectors requiring advanced data processing capabilities. However, translating XQuery into executable SQL poses significant challenges, especially when leveraging large language models (LLMs) locally. The reliance on small datasets and limited fine-tuning approaches constrains the scalability of these translations, particularly for longer or more intricate queries. This limitation is compounded by the methodological inefficiencies inherent in initial prompting strategies, which struggle to generalize beyond simple cases.

Challenges Expansion

The conversion process using locally run LLMs has faced significant hurdles, primarily due to dataset limitations. A small sample size constrains fine-tuning approaches like PEFT on models such as Qwen2.5-Coder 7B, making it difficult to scale for complex queries. Initial successes with parsing-based methods and prompt-engineering are promising but fail to generalize effectively across longer or more intricate XQueries. This indicates a pressing need for improved prompting strategies that can manage the complexity of structured tasks like SQL translation without compromising speed or code quality. The methodological inefficiencies, such as limited dataset size and inadequate fine-tuning approaches, have constrained the scalability of these methods.

Key Specifics Expansion

The dataset constraints significantly impact fine-tuning approaches like PEFT on models such as Qwen2.5-Coder 7B, limiting their effectiveness for complex tasks. Initial successes with parsing-based methods and prompt-engineering are promising but scale poorly for longer queries. Alternative methods, such as structured prompting or different model architectures, show promise for improving performance without sacrificing speed or code quality.

Why It Matters Expansion

The challenges in converting XQuery to SQL using locally run LLMs directly impact the ability of developers to efficiently process complex SQL translations. Current models may not yet be capable of delivering satisfactory performance for real-world applications requiring scalability and efficiency. This turning point underscores the need for innovative approaches beyond fine-tuning, such as enhanced prompting strategies or new model architectures.

Open Questions Expansion

The research brief poses several open questions: whether current models can achieve satisfactory performance with additional data or targeted fine-tuning; the potential effectiveness of integrating structured prompting strategies or exploring alternative architectures; and the robustness of prompting mechanisms across varying query lengths without compromising speed or code quality. Addressing these questions is crucial for advancing the capabilities of locally run LLMs in SQL translation tasks.

Conclusion

The challenges faced by locally run LLMs in converting XQuery to SQL highlight the need for innovative approaches beyond fine-tuning. Expanding on current methods through enhanced prompting strategies and alternative architectures could pave the way for more efficient and scalable solutions, ultimately benefiting developers and organizations seeking cost-effective data translation tools.

Sources

• Converting XQuery to SQL with Local LLMs: Do I Need Fine-Tuning or a Better Approach? [P] — r/MachineLearning
• OpenAI shadow testing GPT 5.5 — r/ChatGPT
• 5.3.System Prompt Issues — r/OpenAI (headline only)

Frequently Asked Questions

Do I need to fine-tune my LLM for converting XQuery to SQL?

Fine-tuning can improve your model's understanding of XQuery syntax, but it may not always be necessary. Consider whether the base model already captures essential aspects of XQuery.

What challenges are involved in converting XQuery to SQL with local LLMs?

Challenges include limited training data for specific query structures and potential performance issues compared to cloud-based solutions.

How can I improve the conversion of XQuery to SQL using my local LLM more effectively?

Fine-tuning or adjusting your approach by restructuring queries can enhance compatibility and performance with local models.

What are some best practices for setting up an XQuery to SQL converter locally?

Ensure your data is well-structured, use prompt engineering to guide the model, and regularly test and refine your approach.

How does converting XQuery to SQL with local LLMs compare to using cloud-based solutions?

Local setups can offer cost savings and faster execution but may require more tailored configurations compared to scalable cloud options.