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 …

Self-correction is an approach to improving responses from large language models (LLMs)
by refining the responses using LLMs during inference. Prior work has proposed various self …

Hundreds of millions of people now interact with language models, with uses ranging from
help with writing, to informing hiring decisions. However, these language models are known …

Recent efforts have incorporated large language models (LLMs) with external resources (eg,
the Internet) or internal control flows (eg, prompt chaining) for tasks requiring grounding or …

Social determinants of health (SDoH) play a critical role in patient outcomes, yet their
documentation is often missing or incomplete in the structured data of electronic health …

Trained on massive publicly available data, large language models (LLMs) have
demonstrated tremendous success across various fields. While more data contributes to …

Careless speech is a new type of harm created by large language models (LLM) that poses
cumulative, long-term risks to science, education and shared social truth in democratic …

Finding agreement through a free exchange of views is often difficult. Collective deliberation
can be slow, difficult to scale, and unequally attentive to different voices. In this study, we …

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

While Reinforcement Learning from Human Feedback (RLHF) aligns Large Language
Models (LLMs) with general, aggregate human preferences, it is suboptimal for learning …