Kathleen Meier-Hellstern — accepted AI-conference papers

2024

Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics

Tyler A. Chang, Katrin Tomanek, Jessica Hoffmann, Nithum Thain, Erin MacMurray van Liemt, Kathleen Meier-Hellstern +1

COLING 2024main

We explore a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia’s Neutral Point of View (NPOV) principle: acknowledge the absence of a single true answer and surface multiple perspectives. We frame this as retrieval augmented generation, where perspectives are retrieved…

Cited by 12 Source ↗PDF ↗Scholar ↗

2022

GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu +21

ICML 2022spotlight

Scaling language models with more data, compute and parameters has driven significant progress in natural language processing. For example, thanks to scaling, GPT-3 was able to achieve strong results on in-context learning tasks. However, training these large dense models requires significant amount…

Cited by 765 Source ↗PDF ↗Scholar ↗