Titre : Understanding Linguistic and Reasoning Mechanisms in Large Language Models
Large language models (LLMs) appear to exhibit human-level abilities on a range of tasks, yet they are notoriously considered to be "black boxes", and little is known about the internal representations and mechanisms that underlie their behavior. This talk will discuss recent work which seeks to illuminate the processing that takes place under the hood. I will focus in particular on questions related to LLM's ability to represent abstract, compositional, and content-independent operations of the type assumed to be necessary for advanced cognitive functioning in humans.
Ellie PAVLICK is an Assistant Professor of Computer Science at Brown University. She received her PhD from University of Pennsylvania in 2017, where her focus was on paraphrasing and lexical semantics. Ellie’s research is on cognitively-inspired approaches to language acquisition, focusing on grounded language learning and on the emergence of structure (or lack thereof) in neural language models. Ellie leads the language understanding and representation (LUNAR) lab, which collaborates with Brown’s Robotics and Visual Computing labs and with the Department of Cognitive, Linguistic, and Psychological Sciences.
Tenney, Ian, Dipanjan Das, and Ellie Pavlick. "BERT Rediscovers the Classical NLP Pipeline." Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019. https://arxiv.org/pdf/1905.05950.pdf
Pavlick, Ellie. "Symbols and grounding in large language models." Philosophical Transactions of the Royal Society A 381.2251 (2023): 20220041. https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2022.0041
Lepori, Michael A., Thomas Serre, and Ellie Pavlick. "Break it down: evidence for structural compositionality in neural networks." arXiv preprint arXiv:2301.10884 (2023). https://arxiv.org/pdf/2301.10884.pdf
Merullo, Jack, Carsten Eickhoff, and Ellie Pavlick. "Language Models Implement Simple Word2Vec-style Vector Arithmetic." arXiv preprint arXiv:2305.16130 (2023). https://arxiv.org/pdf/2305.16130.pdf