Title: Empowering instruction following research with language models as simulators.

Abstract: Instruction-following language models have driven remarkable progress in a range of NLP tasks and have been rapidly adopted across the world. However, academic research into these models has lagged behind due to the lack of open, reproducible, and low-cost environments with which to develop and test instruction-following models. In this talk, I will discuss how new, emerging approaches that study an LLM's ability to emulate human annotators and API endpoints hold promise in improving and critiquing LLMs. To improve instruction-following methods, recent work from our group such as AlpacaFarm shows how an LLM-based simulator can help test scientific hypotheses (e.g. is reinforcement learning helpful?) develop better instruction-following methods, and red-team LLMs in a more open and reproducible way. At the same time, there are major limits to LLMs’ ability to simulate annotators — such as in the opinions they reflect or the consistency of their responses — and we will discuss how these gaps raise important open problems in the trustworthiness of existing LLMs.

Bio: Tatsunori Hashimoto is an Assistant Professor in the Computer Science Department at Stanford University. He is a member of the statistical machine learning and natural language processing groups at Stanford, and his research uses tools from statistics to make machine learning systems more robust and trustworthy — especially in complex systems such as large language models. He is a Kavli fellow, a Sony and Amazon research award winner, and his work has been recognized with best paper awards at ICML and CHI. Before becoming an Assistant Professor, he was a postdoctoral researcher at Stanford with Percy Liang and John Duchi and received his Ph.D. from MIT under the supervision of Tommi Jaakkola and David Gifford.