Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs (2305.03111v3)

Abstract: Text-to-SQL parsing, which aims at converting natural language instructions into executable SQLs, has gained increasing attention in recent years. In particular, Codex and ChatGPT have shown impressive results in this task. However, most of the prevalent benchmarks, i.e., Spider, and WikiSQL, focus on database schema with few rows of database contents leaving the gap between academic study and real-world applications. To mitigate this gap, we present Bird, a big benchmark for large-scale database grounded in text-to-SQL tasks, containing 12,751 pairs of text-to-SQL data and 95 databases with a total size of 33.4 GB, spanning 37 professional domains. Our emphasis on database values highlights the new challenges of dirty database contents, external knowledge between NL questions and database contents, and SQL efficiency, particularly in the context of massive databases. To solve these problems, text-to-SQL models must feature database value comprehension in addition to semantic parsing. The experimental results demonstrate the significance of database values in generating accurate text-to-SQLs for big databases. Furthermore, even the most effective text-to-SQL models, i.e. ChatGPT, only achieves 40.08% in execution accuracy, which is still far from the human result of 92.96%, proving that challenges still stand. Besides, we also provide an efficiency analysis to offer insights into generating text-to-efficient-SQLs that are beneficial to industries. We believe that BIRD will contribute to advancing real-world applications of text-to-SQL research. The leaderboard and source code are available: https://bird-bench.github.io/.

Analysis of "Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs"

The paper undertakes an extensive exploration of text-to-SQL parsing in the context of large-scale database systems, introducing Bird, a benchmark aimed at evaluating the capabilities of LLMs like GPT-4 and Claude-2 as database interfaces. Unlike preceding benchmarks that offer limited rows of database values, Bird simulates a more realistic database scenario with 12,751 text-to-SQL pairs across 95 databases, amounting to 33.4 GB across 37 distinct domains.

Key Contributions

1. Real-World Application Challenges: The Bird benchmark emphasizes challenges prevalent in real-world applications, namely handling databases with noisy and dirty data values, ensuring external knowledge grounding, and enhancing SQL execution efficiency. This focus extends beyond mere schema comprehension to encompass value understanding, which is crucial for accurate query formulation.
2. Performance Analysis: State-of-the-art text-to-SQL models, including GPT-4, exhibit a substantial gap in execution accuracy (54.89%) when compared to human performance (92.96%). This discrepancy highlights the limitations of current models in real-world settings and the formidable challenges posed by the Bird dataset.
3. Efficiency Metric - Valid Efficiency Score (VES): The paper introduces VES as an evaluation metric that prioritizes not only accuracy but also efficiency in SQL generation, offering an incentive for LLMs to generate both precise and fast executable SQL queries.

Experimental Results

Bird sets itself apart by providing a comprehensive assessment of both execution accuracy and SQL efficiency across various models. GPT-4, despite leading in performance, still falls noticeably short of human capabilities. The analysis of execution accuracy alongside VES provides intricate insights into the strengths and persistent weaknesses of contemporary models, especially when faced with extensive and varied data environments.

Implications and Future Directions

The findings underscore the necessity for further research into enhancing LLM's ability to generalize and accurately process extensive database values. The implication is twofold: advancing the semantic understanding and encoding techniques of existing LLMs, and developing innovative training paradigms that integrate value comprehension and contextual reasoning.

The authors speculate on the potential of such research to significantly bridge the gap between model predictions and human performance, thus rendering LLMs more viable as robust interfaces for complex database systems. Bird's benchmarking paradigm also encourages a collaborative approach between the NLP and database communities, aiming to design systems capable of resolving real-world database interaction challenges.

In summary, the research provides a detailed critique of existing text-to-SQL models, illustrating the demanding nature of real-world database tasks. Bird serves as both a resource and challenge, propelling advancements in AI-driven database interfacing and underscoring the continued evolution of LLMs beyond academic boundaries.