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?[…]
Abstract Pre-trained language models form the basis for much of modern NLP, and while the basic pre-training recipe is well known, many details of model development are hidden in secretive research labs. Based on experience[…]
Abstract With language models becoming increasingly embedded in our digital interactions, concerns over data privacy and information leakage are becoming more apparent. The lack of regulation and transparency in how pipelines involving LLMs collect and[…]
