Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. However, it is still very challenging to build highly-reliable applications with LLMs that support specialized use cases. LLMs trained on web data often excel at capturing general language patterns, but they could struggle to support specialized domains and personalized user needs. Moreover, LLMs can produce errors that are deceptively plausible, making them potentially dangerous for high-trust scenarios. In this talk, I will discuss some of our recent efforts in addressing these challenges with data-efficient tuning methods and a novel factuality evaluation framework. Specifically, my talk will focus on building multilingual applications, one crucial use case often characterized by limited tuning and evaluation data.

Bio

Xinyi(Cindy) Wang is a research scientist at Google DeepMind working on Large Language Models(LLM) and its application to generative question-answering. She has worked on multilingual instruction-tuning for Gemini and multilingual generative models used in Google search. Before Google DeepMind, Cindy Wang obtained her PhD degree in Language Technologies at Carnegie Mellon University. During her PhD, she mainly worked on developing data-efficient natural language processing~(NLP) systems. She has made several contributions in data selection, data representation, and model adaptation for multilingual NLP.