Portrait of David Austin

David Austin

PhD candidate · Mila & McGill University

I’m a third-year PhD candidate at Mila and McGill University, advised by Jackie Cheung in the McGill NLP group.

I study how language models take part in the social processes by which people make meaning. Speakers choose among several valid ways of conceptualizing the same thing (e.g. a riot vs. a protest). In humans this choice is negotiated between partners over repeated interaction and can stabilize into conventions across a community. How does this process change when LLMs are conversation partners?

I am pursuing two directions:

  1. Lexical choice in LLMs. What drives how models select a conceptualization in LLM-LLM and LLM-human interaction? Conceptualization affects task success — my Semantic Bandits paper showed that how an environment is labeled changes how LLMs explore it. Outside of toy settings there is rarely one objectively correct labeling.

  2. The role of LLMs in social meaning-making. LLMs offer a new way to interface with cultural knowledge, potentially influencing how we articulate and make sense of our own experience. How can lessons from earlier shifts in cultural technology (writing, print, search) inform the role we envision for LLMs within the larger process of meaning-making? This continues work I pursued at Building 21 at McGill.

Feel free to shoot me an email if these topics align with your interests! I am always happy to chat.

Before Mila, I worked with Scott Sanner and Chris McIntosh at the University of Toronto and the Vector Institute on conversational recommendation and multimodal medical models. I did my undergrad in Computer Science and Statistics at the University of Waterloo, completing research internships with Bo Wang and Doug Lee at the University of Toronto.

Outside of my research, I play guitar and bass, read, and play volleyball. I’m currently very into folk rock like Fairport Convention and Gillian Welch / David Rawlings.

News

Publications

* equal contribution. Also on Google Scholar.

Other

Earlier work in clinical machine learning and statistics.