Large Language Models (LLMs) have demonstrated remarkable success across various
domains. However, despite their promising performance in numerous real-world …

Personalized large language models (LLMs) are designed to tailor responses to individual
user preferences. While Reinforcement Learning from Human Feedback (RLHF) is a …

Reinforcement Learning from Human Feedback (RLHF) is a powerful paradigm for aligning
foundation models to human values and preferences. However, current RLHF techniques …

Large language models (LLMs) appear to bias their survey answers toward certain values.
Nonetheless, some argue that LLMs are too inconsistent to simulate particular values. Are …

GPT-4 demonstrates high accuracy in medical QA tasks, leading with an accuracy of
86.70%, followed by Med-PaLM 2 at 86.50%. However, around 14% of errors remain …

Annotators' sociodemographic backgrounds (ie, the individual compositions of their gender,
age, educational background, etc.) have a strong impact on their decisions when working on …

Toxicity is an increasingly common and severe issue in online spaces. Consequently, a rich
line of machine learning research over the past decade has focused on computationally …

Large language models (LLMs) have shown remarkable promise in simulating human
language use and behavior. In this study, we delve into the intersection of persona variables …

Recent years have seen substantial investments in AI-based tools designed to detect
offensive language at scale, aiming to moderate social media platforms, and ensure safety …

While finetuning language models from pairwise preferences has proven remarkably
effective, the underspecified nature of natural language presents critical challenges. Direct …