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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 …
A Survey of NL2SQL with Large Language Models: Where are we, and where are we going?
Translating users' natural language queries (NL) into SQL queries (ie, NL2SQL) can
significantly reduce barriers to accessing relational databases and support various …
significantly reduce barriers to accessing relational databases and support various …
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 …
Resdsql: Decoupling schema linking and skeleton parsing for text-to-sql
One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the
structural property of the SQL queries, the seq2seq model takes the responsibility of parsing …
structural property of the SQL queries, the seq2seq model takes the responsibility of parsing …
A survey on deep learning approaches for text-to-SQL
To bridge the gap between users and data, numerous text-to-SQL systems have been
developed that allow users to pose natural language questions over relational databases …
developed that allow users to pose natural language questions over relational databases …
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 …
TaBERT: Pretraining for joint understanding of textual and tabular data
Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-
based natural language (NL) understanding tasks. Such models are typically trained on free …
based natural language (NL) understanding tasks. Such models are typically trained on free …
Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
We present Spider, a large-scale, complex and cross-domain semantic parsing and text-to-
SQL dataset annotated by 11 college students. It consists of 10,181 questions and 5,693 …
SQL dataset annotated by 11 college students. It consists of 10,181 questions and 5,693 …
Unifying the perspectives of nlp and software engineering: A survey on language models for code
Z Zhang, C Chen, B Liu, C Liao, Z Gong, H Yu… - arxiv preprint arxiv …, 2023 - arxiv.org
In this work we systematically review the recent advancements in software engineering with
language models, covering 70+ models, 40+ evaluation tasks, 180+ datasets, and 900 …
language models, covering 70+ models, 40+ evaluation tasks, 180+ datasets, and 900 …
The natural language decathlon: Multitask learning as question answering
Deep learning has improved performance on many natural language processing (NLP)
tasks individually. However, general NLP models cannot emerge within a paradigm that …
tasks individually. However, general NLP models cannot emerge within a paradigm that …