SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Scalar Keywords ABS·LENGTH·STRFTIME·JULIADAY·NOW·CAST·SUBSTR·INSTR.
Comparison Keywords  $\bullet = \bullet >\bullet <  \bullet > = \bullet <   = \bullet ! = .$
Computing Keywords  $\bullet -\bullet +\bullet \star \bullet /$
B.13 Study about Text-to-SQL Models
The fundamental principle of a cross-domain text-to-SQL parser involves the construction of an encoder to learn representations of questions and schemas, followed by a decoder to generate SQLs [37]. For example, IRNET [12] designs an encoder consisting of attention-based Bi-LSTM for learning question and schema representations, and a decoder to predict SQLs based on the encoded intermediate representations. RATSQL [43], SDSQL [17], LGESQL [5], and  $\mathbf{S}^2\mathbf{SQ}$  [18], Proton [44] enhance the representation learning of natural language questions and database schema via relational graph neural network.  $\mathbb{R}^2\mathbb{SQ}$  [16], SCORE [54], and STAR [4] enhance contextual learning for conversational text-to-SQL tasks. Later, sequence-to-sequence pre-trained language models (PLMs) such as T5 [38] become popular in text-to-SQL tasks due to their portability and capability of generation across different datasets. These models achieve impressive results by fine-tuning with minimal effort. Furthermore, RASAT [36] enhances T5's structural information encoding via schema alignment into the encoder, while Graphix [27] equips T5 with multi-hop reasoning to achieve state-of-the-art results on complicated cross-domain text-to-SQL tasks. In recent years, LLMs such as ChatGPT [33], Palm [8], OPT [56], have attracted considerable attention due to their powerful zero-shot reasoning and domain generalization capabilities. ChatGPT can perform exceptionally well on semantic parsing tasks, including text-to-SQL tasks, with minimal input data. In fact, in the BIRD project, ChatGPT even performs more impressively than initially expected.
Study about SQL Efficiency Efficient execution of SQL queries on big databases has been a significant topic in both academia and industries. Many techniques are proposed to improve SQL query efficiency, by index selection [22], SQL optimization [26, 61], etc. SQL optimization is a common method for enhancing the efficiency of SQL queries. Several SQL optimization algorithms [28, 30, 47], such as rule-based optimization and cost-based optimization, are proven effective. Rule-based optimization employs a set of principles to transform the SQL query into a form that can be executed more efficiently. On the other hand, cost-based optimization estimates the execution cost of various query plans and selects the one with the lowest cost by analyzing the statistic distribution of database values. Similar to the NLP community, there are also recent works utilizing artificial intelligence for query optimization such as [61]. Index prediction is another important technique for improving SQL execution efficiency. Researchers propose many algorithms of index prediction [60] based on various optimization criteria, such as minimizing SQL execution time, and maximizing index utilization. In this work, we provide VES to measure the efficiency of text-to-SQL generators to encourage them to generate accurate and fast SQLs for users.