Language Models (LLMs) have gained significant traction in various natural language processing tasks due to their ability to generate text that resembles human language. However, LLMs, by their nature, operate by sampling over a probability distribution of words, making them unpredictable and sometimes unsuitable for tasks with strict syntax requirements. In scenarios such as SQL query generation, where precision and accuracy are crucial, relying solely on the model's probabilistic output may not suffice.
A common approach to address the limitations of LLMs is to increase the volume of training data. While this may improve performance to some extent, it often leads to larger model sizes and increased computational requirements. Moreover, even with more data, there's still a lack of control over the model's predictions, making it challenging to ensure accuracy, especially in tasks with rigid syntax constraints.
To tackle this issue, a novel approach called Parsing Incrementally for Constrained Auto-Regressive Decoding (PICARD) was introduced in 2021. PICARD proposes a method where parsing is performed at each step of the generation process, enabling precise control over the output by restricting it to a predefined set of words according to syntax requirements.
The application of PICARD in the context of Text to SQL (Structured Query Language) Language Model (LLM) is particularly promising. The goal is to facilitate users in making SQL queries accurately, regardless of their level of expertise. By implementing PICARD in this domain, the process of generating SQL queries can be simplified, and the accuracy of generated queries can be significantly improved.
The implementation of Text to SQL LLM using PICARD involves two primary steps:
Lexing: In this phase, individual SQL keywords such as SELECT, GROUP, etc., are parsed without considering the values associated with them. This step lays the groundwork for identifying the syntactic elements of the SQL query.
Parsing: Once the lexing phase is complete, the parsed tokens are used to construct the SQL query structure. PICARD ensures that only valid query structures are accepted, rejecting queries with missing clauses or incorrect clause orders. This strict parsing mechanism guarantees the syntactic correctness of the generated SQL queries.
By employing PICARD in the Text to SQL LLM, users can benefit from a more intuitive and accurate querying experience. Whether novice or experienced, users can leverage the simplified process to access and analyze data efficiently. Ultimately, the integration of PICARD enhances the accessibility and usability of SQL query generation, aligning with the overarching goal of making data analysis accessible to everyone.