Millions of everyday users are interacting with technologies built with generative AI, such as voice assistants, search engines, and chatbots. While these AI-based systems are being increasingly integrated into modern life, they can also magnify risks, inequities, and dissatisfaction when providers deploy unreliable systems. A primary obstacle to having reliable systems is the opacity of the underlying large language models— we lack a systematic understanding of how models work, where critical vulnerabilities may arise, why they are happening, and how models must be redesigned to address them. In this talk, I will first describe my work in investigating large language models to illuminate when and how they acquire knowledge and capabilities. Then, I will describe my work on building methods to enable greater data transparency for large language models, that allows stakeholders to make sense of the information available to models. Finally, I will describe my work on understanding how this information can get distorted in large language models, and implications for building the next generation of robust AI systems. 

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AG Audience

English

60 Minutes

Saarbrücken

Abhilasha Ravichander is a postdoctoral scholar at the University of Washington, advised by Professor Yejin Choi.  She received her PhD from Carnegie Mellon University in 2022. Her research spans natural language processing, machine learning, and artificial intelligence, with a focus on improving the robustness and interpretability of large-scale language models.  Abhilasha’s work has been presented at several top NLP conferences, receiving Best Resource Paper Award at ACL 2024, Best Theme Paper Award at ACL 2024, Best Paper Award at the Mid-Atlantic Student Colloquium 2024, Best Paper Award at the SoCalNLP 2022 symposium,, and Area Chair Favorite Paper award at COLING 2018.  She has been recognized as a "Rising Star in Generative AI" (2024), "Rising Star in EECS" (2022), and "Rising Star in Data Science" (2021).  She served as co-chair of the socio-cultural inclusion committee for NAACL 2022, area chair for EMNLP, ACL, and ARR, and received the outstanding reviewer recognition at ACL 2020 and EMNLP 2020.

Gretchen Gravelle

+49 681 9303 9102

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