A survey on text-to-sql parsing: Concepts, methods, and future directions
Text-to-SQL parsing is an essential and challenging task. The goal of text-to-SQL parsing is
to convert a natural language (NL) question to its corresponding structured query language …
to convert a natural language (NL) question to its corresponding structured query language …
Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls
Text-to-SQL parsing, which aims at converting natural language instructions into executable
SQLs, has gained increasing attention in recent years. In particular, GPT-4 and Claude-2 …
SQLs, has gained increasing attention in recent years. In particular, GPT-4 and Claude-2 …
Graphix-t5: Mixing pre-trained transformers with graph-aware layers for text-to-sql parsing
The task of text-to-SQL parsing, which aims at converting natural language questions into
executable SQL queries, has garnered increasing attention in recent years. One of the major …
executable SQL queries, has garnered increasing attention in recent years. One of the major …
Rasat: Integrating relational structures into pretrained seq2seq model for text-to-sql
Relational structures such as schema linking and schema encoding have been validated as
a key component to qualitatively translating natural language into SQL queries. However …
a key component to qualitatively translating natural language into SQL queries. However …
SSQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers
The task of converting a natural language question into an executable SQL query, known as
text-to-SQL, is an important branch of semantic parsing. The state-of-the-art graph-based …
text-to-SQL, is an important branch of semantic parsing. The state-of-the-art graph-based …
Large language models are versatile decomposers: Decomposing evidence and questions for table-based reasoning
Table-based reasoning has shown remarkable progress in a wide range of table-based
tasks. It is a challenging task, which requires reasoning over both free-form natural language …
tasks. It is a challenging task, which requires reasoning over both free-form natural language …
LI-EMRSQL: Linking information enhanced Text2SQL parsing on complex electronic medical records
Converting natural language text into executable SQL queries significantly impacts the
healthcare domain, specifically when applied to electronic medical records. Given that …
healthcare domain, specifically when applied to electronic medical records. Given that …
Proton: Probing schema linking information from pre-trained language models for text-to-sql parsing
The importance of building text-to-SQL parsers which can be applied to new databases has
long been acknowledged, and a critical step to achieve this goal is schema linking, ie …
long been acknowledged, and a critical step to achieve this goal is schema linking, ie …
Few-shot text-to-sql translation using structure and content prompt learning
A common problem with adopting Text-to-SQL translation in database systems is poor
generalization. Specifically, when there is limited training data on new datasets, existing few …
generalization. Specifically, when there is limited training data on new datasets, existing few …
Space-2: Tree-structured semi-supervised contrastive pre-training for task-oriented dialog understanding
Pre-training methods with contrastive learning objectives have shown remarkable success
in dialog understanding tasks. However, current contrastive learning solely considers the …
in dialog understanding tasks. However, current contrastive learning solely considers the …