Abstract: NLP models often rely on human-labeled data for training and evaluation. Many approaches crowdsource this data from a large number of annotators with varying skills, backgrounds, and motivations, resulting in conflicting annotations. These conflicts[…]
Abstract Millions of people around the world query (prompt) large language models for information. While several studies have compellingly documented the persuasive potential of these models, there is limited evidence of who or what influences[…]
Abstract: The widespread adoption of large language models (LLMs) places a responsibility on the AI research community to rigorously study and understand them. In this talk, I will describe my group’s research on analyzing LLMs’[…]
Abstract: Advanced AI systems are being deployed for more and more complex tasks. To ensure reliable human oversight over AIs, we need supervision protocols that remain effective despite the increase in task complexity and model[…]
Abstract Speech contains all the lexical content of text in addition to other features like pitch and prosody. Could this type of non-lexical information be useful for tasks like summarization? I will discuss recent work[…]
Abstract Humans don’t learn intelligence—they develop it. Biological life on Earth benefits from innate cognitive primitives encoded in its genes. Cats don’t learn to backflip, just as birds don’t learn to fly. Likewise, humans don’t[…]
Abstract Large language models (LLMs) have rapidly and impressively taken over many tasks once handled by human annotators in constructing text datasets. But does this mean we no longer need humans in the annotation loop?[…]
