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 …

Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …

We present a holistic approach to building a robust and useful natural language
classification system for real-world content moderation. The success of such a system relies …

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 …

Majority voting and averaging are common approaches used to resolve annotator
disagreements and derive single ground truth labels from multiple annotations. However …

Generative AI systems across modalities, ranging from text (including code), image, audio,
and video, have broad social impacts, but there is no official standard for means of …

Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks
ranging from online comment toxicity to misinformation detection to medical diagnosis …

Natural language generation has witnessed significant advancements due to the training of
large language models on vast internet-scale datasets. Despite these advancements, there …

Human feedback plays a central role in the alignment of Large Language Models (LLMs).
However, open questions remain about the methods (how), domains (where), people (who) …