Search Results for author: Kathleen Meier-Hellstern

Found 3 papers, 1 papers with code

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

no code implementations • 13 Mar 2024 • Tyler A. Chang, Katrin Tomanek, Jessica Hoffmann, Nithum Thain, Erin Van Liemt, Kathleen Meier-Hellstern, Lucas Dixon

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.

Hallucination Retrieval +1

Paper
Add Code

LaMDA: Language Models for Dialog Applications

2 code implementations • 20 Jan 2022 • Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, Yaguang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, Quoc Le

We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.

Ranked #114 on Code Generation on HumanEval

Code Generation Information Retrieval +2

458

Paper
Code

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

no code implementations • 13 Dec 2021 • Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V Le, Yonghui Wu, Zhifeng Chen, Claire Cui

Scaling language models with more data, compute and parameters has driven significant progress in natural language processing.

Ranked #10 on Language Modelling on LAMBADA

Common Sense Reasoning In-Context Learning +2

Paper
Add Code

Cannot find the paper you are looking for? You can Submit a new open access paper.