We show that a GPT-3 model can learn to express uncertainty about its own answers in
natural language--without use of model logits. When given a question, the model generates …

Forecasting future events is important for policy and decision making. In this work, we study
whether language models (LMs) can forecast at the level of competitive human forecasters …

For billions of years, evolution has been the driving force behind the development of life,
including humans. Evolution endowed humans with high intelligence, which allowed us to …

Temporal reasoning is a crucial natural language processing (NLP) task, providing a
nuanced understanding of time-sensitive contexts within textual data. Although recent …

As artificial intelligence systems grow more powerful, there has been increasing interest in"
AI safety" research to address emerging and future risks. However, the field of AI safety …

Temporal knowledge graph (TKG) forecasting benchmarks challenge models to predict
future facts using knowledge of past facts. In this paper, we apply large language models …

If machine learning models were to achieve superhuman abilities at various reasoning or
decision-making tasks, how would we go about evaluating such models, given that humans …

As AI technology is rapidly progressing in capability and being adopted more widely across
society, it is more important than ever to understand the potential risks AI may pose and how …

This study investigates the forecasting accuracy of human experts versus large language
models (LLMs) in the retail sector, particularly during standard and promotional sales …

Recent advancements in Large Language Models (LLMs) have empowered LLM agents to
autonomously collect world information, over which to conduct reasoning to solve complex …