In many scientific fields, large language models (LLMs) have revolutionized the way text and
other modalities of data (eg, molecules and proteins) are handled, achieving superior …

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 …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

Recent literature has shown that large language models (LLMs) are generally excellent few-
shot reasoners to solve text reasoning tasks. However, the capability of LLMs on table …

Language models have shown promising performance on the task of translating natural
language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art …

Text-to-SQL, the process of translating natural language into Structured Query Language
(SQL), represents a transformative application of large language models (LLMs), potentially …

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 …

This work aims to tackle the challenging heterogeneous graph encoding problem in the text-
to-SQL task. Previous methods are typically node-centric and merely utilize different weight …

The de-facto standard decoding method for semantic parsing in recent years has been to
autoregressively decode the abstract syntax tree of the target program using a top-down …

Large language models (LLMs) with in-context learning have demonstrated remarkable
capability in the text-to-SQL task. Previous research has prompted LLMs with various …