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 …

Machine Reading Comprehension (MRC) is a challenging task and hot topic in Natural
Language Processing. The goal of this field is to develop systems for answering the …

We explore how generating a chain of thought---a series of intermediate reasoning steps---
significantly improves the ability of large language models to perform complex reasoning. In …

Neuro-Symbolic Artificial Intelligence–the combination of symbolic methods with methods
that are based on artificial neural networks–has a long-standing history. In this article, we …

A recent focus of large language model (LLM) development, as exemplified by generative
search engines, is to incorporate external references to generate and support its claims …

We propose a new paradigm to help Large Language Models (LLMs) generate more
accurate factual knowledge without retrieving from an external corpus, called RECITation …

The sheer volume of financial statements makes it difficult for humans to access and analyze
a business's financials. Robust numerical reasoning likewise faces unique challenges in this …

Answering complex questions that require making latent decisions is a challenging task,
especially when limited supervision is available. Recent works leverage the capabilities of …

CommonsenseQA (CQA)(Talmor et al., 2019) dataset was recently released to advance the
research on common-sense question answering (QA) task. Whereas the prior work has …

Large pre-trained language models (LMs) are known to encode substantial amounts of
linguistic information. However, high-level reasoning skills, such as numerical reasoning …