AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …

Large language models (LLMs) have shown powerful performance and development
prospect and are widely deployed in the real world. However, LLMs can capture social …

We report the development of GPT-4, a large-scale, multimodal model which can accept
image and text inputs and produce text outputs. While less capable than humans in many …

Recently, there has been an increased interest in demographically grounded bias in natural
language processing (NLP) applications. Much of the recent work has focused on describing …

We survey 146 papers analyzing" bias" in NLP systems, finding that their motivations are
often vague, inconsistent, and lacking in normative reasoning, despite the fact that …

It is well documented that NLP models learn social biases, but little work has been done on
how these biases manifest in model outputs for applied tasks like question answering (QA) …

Recent advances in deep learning techniques have enabled machines to generate
cohesive open-ended text when prompted with a sequence of words as context. While these …

In the last year, new models and methods for pretraining and transfer learning have driven
striking performance improvements across a range of language understanding tasks. The …

Pretrained language models, especially masked language models (MLMs) have seen
success across many NLP tasks. However, there is ample evidence that they use the cultural …

We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes
(+ 3.9 F1) and GAP (+ 11.5 F1) benchmarks. A qualitative analysis of model predictions …