A survey on deep learning for software engineering
In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …
and an improved model training method to break the bottleneck of neural network …
Towards natural language interfaces for data visualization: A survey
Utilizing Visualization-oriented Natural Language Interfaces (V-NLI) as a complementary
input modality to direct manipulation for visual analytics can provide an engaging user …
input modality to direct manipulation for visual analytics can provide an engaging user …
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 …
Din-sql: Decomposed in-context learning of text-to-sql with self-correction
M Pourreza, D Rafiei - Advances in Neural Information …, 2024 - proceedings.neurips.cc
There is currently a significant gap between the performance of fine-tuned models and
prompting approaches using Large Language Models (LLMs) on the challenging task of text …
prompting approaches using Large Language Models (LLMs) on the challenging task of text …
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 …
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 …
Text-to-sql empowered by large language models: A benchmark evaluation
Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL task.
However, the absence of a systematical benchmark inhibits the development of designing …
However, the absence of a systematical benchmark inhibits the development of designing …
Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers
When translating natural language questions into SQL queries to answer questions from a
database, contemporary semantic parsing models struggle to generalize to unseen …
database, contemporary semantic parsing models struggle to generalize to unseen …
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 …
Bridging textual and tabular data for cross-domain text-to-SQL semantic parsing
We present BRIDGE, a powerful sequential architecture for modeling dependencies
between natural language questions and relational databases in cross-DB semantic …
between natural language questions and relational databases in cross-DB semantic …