We survey recent work on neurosymbolic programming, an emerging area that bridges the
areas of deep learning and program synthesis. Like in classic machine learning, the goal …

Program synthesis strives to generate a computer program as a solution to a given problem
specification, expressed with input-output examples or natural language descriptions. The …

Most existing benchmarks for grounding language in interactive environments either lack
realistic linguistic elements, or prove difficult to scale up due to substantial human …

Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL)
ability, where the model learns to do an unseen task simply by conditioning on a prompt …

Large pre-trained language models have been used to generate code, providing a flexible
interface for synthesizing programs from natural language specifications. However, they …

Language models, especially pre-trained large language models, have showcased
remarkable abilities as few-shot in-context learners (ICL), adept at adapting to new tasks …

Human reasoning can be understood as an interplay between two systems: the intuitive and
associative (" System 1") and the deliberative and logical (" System 2"). Neural sequence …

Recently, large language models (LLMs), especially those that are pretrained on code, have
demonstrated strong capabilities in generating programs from natural language inputs …

We describe a class of tasks called decision-oriented dialogues, in which AI assistants such
as large language models (LMs) must collaborate with one or more humans via natural …

Neural models greatly outperform grammar-based models across many tasks in modern
computational linguistics. This raises the question of whether linguistic principles, such as …