Trustworthy Optimization of Pre-Trained Models for Healthcare

Generalizability, Adaptability, and Security

Abstract

Pre-trained language models have opened new possibilities in healthcare, showing promise in mining scientific literature, analyzing large-scale clinical data, identifying patterns in emerging diseases, and automating workflows, positioning themselves as intelligent research assistants. However, general-purpose models, typically trained on web-scale corpora, often lack the clinical grounding necessary for reliable deployment in high-stakes domains like healthcare. To be effective, they must be adapted to meet domain-specific requirements. My PhD thesis addresses three core challenges in leveraging pre-trained models for healthcare: (i) the scarcity of labeled data for fine-tuning, (ii) the evolving nature of healthcare data, and (iii) the need to ensure transparency and traceability of AI-generated content. In this talk, I will focus on the third challenge: enabling traceability of content generated by large language models. I will begin with an overview of prior watermarking approaches and then present our proposed solution. We introduce a watermarking algorithm applied at inference time that perturbs the model’s logits to bias generation toward a subset of vocabulary tokens determined by a secret key. To ensure that watermarking does not compromise generation quality, we propose a multi-objective optimization (MOO) framework that employs lightweight networks to produce token-specific watermarking logits and splitting ratios, specifying how many tokens to bias and by how much. This approach effectively balances watermark detectability with semantic coherence. Experimental results show that our method significantly improves detectability and robustness against removal attacks while preserving the semantics of the generated text, outperforming existing watermarking techniques.

Video

https://universityofexeter.zoom.us/clips/share/fWdt2Tm7SQ2Sbqu9KlhKvw

Speaker’s bio

Dr. Sai Ashish Somayajula is a Senior Applied Scientist in Generative AI at Oracle Cloud Infrastructure, where he develops large-scale foundation models for enterprise applications. He earned his PhD in Electrical and Computer Engineering from the University of California (UC), San Diego. His research focused on addressing key challenges in adapting and utilizing pre-trained models for healthcare. Specifically, his work spanned three core areas: (1) synthetic data generation using meta-learning-based feedback mechanisms, (2) continual learning for handling dynamic data streams without catastrophic forgetting, and (3) token-level watermarking techniques to ensure content provenance and security. His research has been published in premier venues, including the International Conference on Machine Learning (ICML), Annual Meeting of the Association for Computational Linguistics (ACL), Transactions of the Association for Computational Linguistics (TACL), Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Scientific Reports (Nature Portfolio), and Transactions of Machine Learning Research (TMLR). He is a recipient of the Jacobs School of Engineering Departmental Fellowship at UC San Diego. Ashish has collaborated with leading industrial research labs through internships at Apple and Tencent AI Lab. He holds a Bachelor’s degree in Electrical Engineering with a minor in Computer Science from the Indian Institute of Technology, Hyderabad, where he was twice awarded the Academic Excellence Award, and a Master’s in Intelligent Systems and Robotics from UC San Diego.