Large language models (LLMs) have demonstrated remarkable capabilities across a wide
range of tasks in various domains. Despite their impressive performance, they can be …

This article presents a comprehensive and practical guide for practitioners and end-users
working with Large Language Models (LLMs) in their downstream Natural Language …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

The increasing scale of large language models (LLMs) brings emergent abilities to various
complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is …

Human variation in labeling is often considered noise. Annotation projects for machine
learning (ML) aim at minimizing human label variation, with the assumption to maximize …

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 …

Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …

Tuning pre-trained language models (PLMs) with task-specific prompts has been a
promising approach for text classification. Particularly, previous studies suggest that prompt …

The increased deployment of LMs for real-world tasks involving knowledge and facts makes
it important to understand model epistemology: what LMs think they know, and how their …

Recent works have shown that language models (LM) capture different types of knowledge
regarding facts or common sense. However, because no model is perfect, they still fail to …