Improving Multi-Turn Text-to-SQL with CoE-SQL

In recent years, large language models (LLMs) have shown they can handle a range of tasks with great skill. One area where they shine is in converting natural language questions into SQL queries, a task known as Text-to-SQL. This is especially useful when users want to retrieve information from databases without needing to understand complex database languages. In this article, we will look at how conversations can affect this translation and introduce a method called CoE-SQL that improves multi-turn text-to-SQL tasks.

#What is Text-to-SQL?

Text-to-SQL is a process that translates human questions into SQL queries. This is important because it allows everyday users to interact with databases without needing to learn SQL. For example, if someone asks, "What are all the parties?" the system should respond with an appropriate SQL query, such as SELECT * FROM party.

#Multi-turn Conversations

In many real-world scenarios, users do not just ask one question and stop. Instead, they often have ongoing conversations. Each question builds on the previous ones. For instance, after asking about all the parties, a user may follow up with, "Order them by the number of hosts." This means that the new SQL query is connected to the previous one, and the system must be smart enough to consider context when constructing the next query.

#Current Challenges

Existing text-to-SQL systems often struggle with this multi-turn aspect. Most traditional methods focus on single questions and are not designed to handle the context of previous questions effectively. As a result, these systems can misinterpret the user's intent or forget important details from earlier interactions. For example, if a previous question specified "employees under age 30," the system might overlook this when answering a follow-up question.

#The CoE-SQL Method

To address these challenges, we introduce CoE-SQL, a method designed to enhance the reasoning of large language models in multi-turn text-to-SQL tasks. The main idea is to treat the SQL editing process like a conversation, where each new query builds on the previous SQL statement. This method allows for small adjustments to be made rather than generating entirely new queries from scratch.

#The Chain of Editions Concept

CoE-SQL uses a concept called "Chain of Editions," which means that when a new SQL query is needed, the system revisits the previous query and applies specific changes or edits to it. This process captures the user's evolving needs without having to start from scratch.

For example, if the first SQL query retrieves all parties, the next query that orders these parties by the number of hosts can simply modify the earlier query. This method saves time and reduces the chances of error since the foundation of the new query already exists.

#How CoE-SQL Works

#Step 1: Defining Edit Rules

To effectively edit SQL queries, we need clear rules that outline how to make changes. For instance, rules can dictate how to add new columns to the SELECT clause or how to change the sorting order in the ORDER BY clause. A total of 14 different edit rules have been identified to cover a range of possible edits.

#Step 2: Extracting the Edition Chain

Once the user asks a question, the system analyzes the last SQL query. By comparing the new question with the last one, the system determines what needs to change to produce the new SQL query. This comparison involves using something called an Abstract Syntax Tree (AST) to identify differences between the two queries. The edition chain is built based on these differences.

#Step 3: Applying Styles for Clarity

CoE-SQL can present the edit chain in various styles, such as natural language or as technical commands. After testing, it was found that presenting the edits in natural language had the best results, as it aligns well with how large language models were trained.

#Experimentation and Results

To evaluate the effectiveness of CoE-SQL, extensive experiments were conducted using two main benchmarks, SParC and CoSQL. These benchmarks consist of multiple conversation sequences and SQL queries that allow for a robust testing ground.

#Comparison with Other Methods

CoE-SQL was compared against other text-to-SQL models, showing significant improvements in performance. Specifically, it was found to generate more accurate SQL queries in multi-turn contexts. While traditional models struggled with context, CoE-SQL’s editing approach allowed it to maintain accuracy across multiple dialogue turns.

#Evaluation Metrics

The performance was measured using several metrics:

• Exact Match Accuracy (EM): Whether each part of the generated SQL matches the correct SQL.
• Execution Accuracy (EX): This checks if the generated SQL correctly retrieves the intended results from the database.
• Test-Suite Accuracy (TS): This evaluates the accuracy based on multiple database instances.

CoE-SQL achieved strong numbers in these areas, especially in EX, demonstrating its effectiveness in generating valid SQL queries.

#Case Studies

Several case studies demonstrated the advantages of the CoE-SQL approach. In one example, after a user asked about all parties, the system successfully retained previously stated conditions in subsequent queries. This ability to track context through conversation turns highlighted CoE-SQL's strength compared to baseline methods that often forget crucial details.

#Limitations and Future Work

While CoE-SQL shows promise, there are limitations. The focus was mainly on the edition chain, without delving into the best practices for exemplar selection, which can greatly affect performance. Additionally, there remains room for improvement in optimizing the CoE-SQL approach further.

Future work may focus on refining these aspects, exploring how different ways of selecting examples can help improve outcomes. Other potential avenues for development include addressing the remaining gaps in performance when compared to fine-tuned models.

#Conclusion

CoE-SQL marks a significant step forward in the multi-turn text-to-SQL domain. By treating SQL query generation as an editing process based on prior interactions, this method allows for more accurate and efficient translations of user intent into valid SQL queries. The results from comprehensive testing show that it not only outperforms traditional models but also keeps pace with advanced fine-tuned models. As this area continues to grow, CoE-SQL represents a promising direction for future research and application.

#References