Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Large language models (LLMs) are a special class of pretrained language models (PLMs)
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …

Spurred by advancements in scale, large language models (LLMs) have demonstrated the
ability to perform a variety of natural language processing (NLP) tasks zero-shot--ie, without …

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

Reinforcement learning from human feedback (RLHF) has proven effective in aligning large
language models (LLMs) with human preferences. However, gathering high-quality human …

Evaluating the factuality of long-form text generated by large language models (LMs) is non-
trivial because (1) generations often contain a mixture of supported and unsupported pieces …

This study examines the performance of open-source Large Language Models (LLMs) in
text annotation tasks and compares it with proprietary models like Chat-GPT and human …

Large Language Models (LLMs) have made remarkable strides in various tasks. Whether
LLMs are competitive few-shot solvers for information extraction (IE) tasks, however, remains …

This paper delves into the pivotal role of prompt engineering in unleashing the capabilities
of Large Language Models (LLMs). Prompt engineering is the process of structuring input …

Recent accelerations in multi-modal applications have been made possible with the
plethora of image and text data available online. However, the scarcity of analogous data in …