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 work on the limitations of using reinforcement learning from human feedback (RLHF)
to incorporate human preferences into model behavior often raises social choice theory as a …

This whitepaper offers an overview of the ethical considerations surrounding research into
or with large language models (LLMs). As LLMs become more integrated into widely used …

Game-playing agents like AlphaGo have achieved superhuman performance through self-
play, which is theoretically guaranteed to yield optimal policies in competitive games …

Despite recent advances in large language models, building dependable and deployable
NLP models typically requires abundant, high-quality training data. However, task-specific …

Annotation quality is often framed as post-hoc cleanup of annotator-caused issues. This
position paper discusses whether, how, and why this narrative limits the scope of improving …

Fairness-related assumptions about what constitute appropriate NLG system behaviors
range from invariance, where systems are expected to behave identically for social groups …

Longstanding data labeling practices in machine learning involve collecting and
aggregating labels from multiple annotators. But what should we do when annotators …

Gender-Based Violence (GBV) is an increasing problem online, but existing datasets fail to
capture the plurality of possible annotator perspectives or ensure the representation of …

Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research
goal that involves creating agents that can sense, perceive, reason about, learn from, and …