OATML

Daniella (Zihuiwen) Ye is a DPhil student in Computer Science at the University of Oxford, supervised by Yarin Gal and Phil Blunsom. Her research interests lie in enhancing the reliability and robustness of Large Language Models (LLMs) for real-world applications, with a particular focus on text generation. She is also interested in exploring methods of controlled generation of language models, employing statistical and linguistic approaches.

Some of the topics she has researched in previous years include improving human preference learning for LLMs with synthetic critiques, a project she undertook during her internship at Cohere. She has also explored augmenting text-to-code generation processes through self-play, and applying diffusion models for non-autoregressive text planning. She is a recipient of the DeepMind scholarship.