YaGuang Li

YaGuang Li

YaGuang Li a senior staff research engineer in the Google DeepMind, He co-led the finetuning effort of Gemini App. He is also core contributor of LaMDA, PaLM-2 and Gemini working on pre-training, instruction tuning and improving serving efficiency. 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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    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. View details
    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
    Hongrae Lee
    Igor Krivokon
    James Qin
    Jamie Hall
    Joe Fenton
    Johnny Soraker
    Lora Mois Aroyo
    Maarten Paul Bosma
    Marc Joseph Pickett
    Marcelo Amorim Menegali
    Marian Croak
    Maxim Krikun
    Meredith Ringel Morris
    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. View details
    Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
    Rose Yu
    Cyrus Shahabi
    Yan Liu
    International Conference on Learning Representations (ICLR)(2018)
    Preview abstract Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and (3) inherent difficulty of long-term forecasting. To address these challenges, we propose to model the traffic flow as a diffusion process on a directed graph and introduce Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow. Specifically, DCRNN captures the spatial dependency using bidirectional random walks on the graph, and the temporal dependency using the encoder-decoder architecture with scheduled sampling. We evaluate the framework on two real-world large scale road network traffic datasets and observe consistent improvement of 12% - 15% over state-of-the-art baselines. View details