YaGuang Li
YaGuang Li a staff research engineer in the Google DeepMind, Brain team working on improving serving efficiency and instruction tuning for large language models such as LaMDA/Bard, PaLM. Prior to joining Google, YaGuang received his Ph.D. degree in Computer Science at the University of Southern California and his Master degree in Computer Science from Institute of Software in University of Chinese Academy of Sciences in 2014.
Authored Publications
Google Publications
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LaMDA: Language Models for Dialog Applications
Aaron Daniel Cohen
Alena Butryna
Alicia Jin
Apoorv Kulshreshtha
Ben Zevenbergen
Chung-ching Chang
Cosmo Du
Daniel De Freitas Adiwardana
Dehao Chen
Dmitry (Dima) Lepikhin
Erin Hoffman-John
Igor Krivokon
James Qin
Jamie Hall
Joe Fenton
Johnny Soraker
Maarten Paul Bosma
Marc Joseph Pickett
Marcelo Amorim Menegali
Marian Croak
Maxim Krikun
Noam Shazeer
Rachel Bernstein
Ravi Rajakumar
Ray Kurzweil
Romal Thoppilan
Steven Zheng
Taylor Bos
Toju Duke
Tulsee Doshi
Vincent Y. Zhao
Will Rusch
Yuanzhong Xu
arXiv (2022)
Preview abstract
We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and arepre-trained on 1.56T words of public dialog data and web text. While model scaling alone canimprove quality, it shows less improvements on safety and factual grounding. We demonstrate thatfine-tuning with annotated data and enabling the model to consult external knowledge sources canlead to significant improvements towards the two key challenges of safety and factual grounding.The first challenge, safety, involves ensuring that the model’s responses are consistent with a set ofhuman values, such as preventing harmful suggestions and unfair bias. We quantify safety using ametric based on an illustrative set of values, and we find that filtering candidate responses using aLaMDA classifier fine-tuned with a small amount of crowdworker-annotated data offers a promisingapproach to improving model safety. The second challenge, factual grounding, involves enabling themodel to consult external knowledge sources, such as an information retrieval system, a languagetranslator, and a calculator. We quantify factuality using a groundedness metric, and we find that ourapproach enables the model to generate responses grounded in known sources, rather than responsesthat merely sound plausible. Finally, we explore the use of LaMDA in the domains of education andcontent recommendations, and analyze their helpfulness and role consistency.
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HyperPrompt: Prompt-based Task-Conditioning of Transformers
Cosmo Du
Steven Zheng
Vamsi Aribandi
Yi Tay
Yun He
Zhao Chen
ICML (2022)
Preview abstract
Prompt-tuning is becoming a new paradigm for finetuning pre-trained language models in a parameter-efficient way. Here, we explore the use of HyperNetworks to generate prompts. We propose a novel architecture of HyperPrompt: prompt-based task-conditioned parameterization of self-attention in Transformers. We show that HyperPrompt is very competitive against strong multi-task learning baselines with only 1% of additional task-conditioning parameters. The prompts are end-to-end learnable via generation by a HyperNetwork. The additional parameters scale sub-linearly with the number of downstream tasks, which makes it very parameter efficient for multi-task learning. Hyper-Prompt allows the network to learn task-specific feature maps where the prompts serve as task global memories. Information sharing is enabled among tasks through the HyperNetwork to alleviate task conflicts during co-training. Through extensive empirical experiments, we demonstrate that HyperPrompt can achieve superior performances over strong T5 multi-task learning base-lines and parameter-efficient adapter variants including Prompt-Tuning on Natural Language Understanding benchmarks of GLUE and Super-GLUE across all the model sizes explored.
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Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Rose Yu
Cyrus Shahabi
Yan Liu
International Conference on Learning Representations (ICLR) (2018)
