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Hongrae Lee

Hongrae Lee

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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. View details
    Preview abstract Descriptive titles provide crucial context for interpreting tables that are extracted from web pages and are a key component of search features such as tabular featured snippets from Google and Bing. Prior approaches have attempted to produce titles by selecting existing text snippets associated with the table. These approaches, however, are limited by their dependence on suitable titles existing a priori. In our user study, we observe that the relevant information for the title tends to be scattered across the page, and often-more than 80% of the time-does not appear verbatim anywhere in the page. We propose instead the application of a sequence-to-sequence neural network model as a more generalizable approach for generating high-quality table titles. This is accomplished by extracting many text snippets that have potentially relevant information to the table, encoding them into an input sequence, and using both copy and generation mechanisms in the decoder to balance relevance and readability of the generated title. We validate this approach with human evaluation on sample web tables and report that while sequence models with only a copy mechanism or only a generation mechanism are easily outperformed by simple selection-based baselines, the model with both capabilities performs the best, approaching the quality of crowdsourced titles while training on fewer than ten thousand examples. To the best of our knowledge, the proposed technique is the first to consider text-generation methods for table titles, and establishes a new state of the art. View details
    Ten Years of Web Tables
    Michael J. Cafarella
    Alon Halevy
    Cong Yu
    Daisy Zhe Wang
    Eugene Wu
    PVLDB (2018)
    Preview abstract In 2008, we wrote about WebTables, an effort to exploit the large and diverse set of structured databases casually published online in the form of HTML tables. The pastdecade has seen a flurry of research and commercial activity around the WebTables project itself, as well as the broad topic of informal online structured data. As exciting as the past decade as been, we think the next ten years hold evenmore promise. In this paper, we will review the WebTables project, and try to place it in the broader context ofthe decade of work that followed. We will also propose an agenda for the next ten exciting years of work, a project that can draw upon many unexpected corners of the data management community View details
    Using SSDs to scale up Google Fusion Tables, a Database-in-the-Cloud
    Yingyi Bu
    Changkyu Kim
    32nd IEEE International Conference on Data Engineering, ICDE 2016, Helsinki, Finland, May 16-20, 2016, {IEEE} Computer Society, pp. 1263-1274
    Preview abstract Flash memory solid state drives (SSDs) have increasingly been advocated and adopted as a means of speeding up and scaling up data-driven applications. SSDs are becoming more widely available as an option in the cloud. However, when an application considers SSDs in the cloud, the best option for the application may not be immediate, among a number of choices for placing SSDs in the layers of the cloud. Although there have been many studies on SSDs, they often concern a specific setting, and how different SSD options in the cloud compare with each other is less well understood. In this paper, we describe how Google Fusion Tables (GFT) used SSDs and what optimizations were implemented to scale up its in-memory processing, clearly showing opportunities and limitations of SSDs in the cloud with quantitative analyses. We first discuss various SSD placement strategies and compare them with low-level measurements, and propose SSD-placement guidelines for a variety of cloud data services. We then present internals of our column engine and optimizations to better use the performance characteristics of SSDs. We empirically demonstrate that the optimizations enable us to scale our application to much larger datasets while retaining the low-latency and simple query processing architecture. View details
    Applying WebTables in Practice
    Sreeram Balakrishnan
    Alon Halevy
    Boulos Harb
    Warren Shen
    Kenneth Wilder
    Fei Wu
    Cong Yu
    Conference on Innovative Data Systems Research (2015)
    Preview
    Mining Subjective Properties on the Web
    Immanuel Trummer
    Alon Halevy
    Sunita Sarawagi
    SIGMOD (2015) (to appear)
    Preview
    Efficient spatial sampling of large geographical tables
    Anish Das Sarma
    Hector Gonzalez
    Alon Halevy
    Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, ACM, New York, NY, USA, pp. 193-204
    Preview
    Big Data Storytelling Through Interactive Maps
    Sreeram Balakrishnan
    Kathryn Hurley
    Hector Gonzalez
    Nitin Gupta
    Alon Halevy
    Karen Jacqmin-Adams
    Anno Langen
    Rod McChesney
    Rebecca Shapley
    Warren Shen
    IEEE Data Engineering Bulletin, vol. 35 (2012), pp. 46-54
    Preview
    Finding Related Tables
    Anish Das Sarma
    Lujun Fang
    Nitin Gupta
    Alon Y. Halevy
    Fei Wu
    Reynold Xin
    Cong Yu
    SIGMOD (2012)
    Preview abstract We consider the problem of finding related tables in a large corpus of heterogenous tables. Detecting related tables provides users a powerful tool for enhancing their tables with additional data and enables effective reuse of available public data. Our first contribution is a framework that captures several types of relatedness, including tables that are candidates for joins and tables that are candidates for union. Our second contribution is a set of algorithms for detecting related tables that can be either unioned or joined. We describe a set of experiments that demonstrate that our algorithms produce highly related tables. We also show that we can often improve the results of table search by pulling up tables that are ranked much lower based on their relatedness to top-ranked tables. Finally, we describe how to scale up our algorithms and show the results of running it on a corpus of over a million tables extracted from Wikipedia. View details
    Efficient Spatial Sampling of Large Geographical Tables
    Anish Das Sarma
    Hector Gonzalez
    Alon Y. Halevy
    SIGMOD (2012)
    Preview abstract Large-scale map visualization systems play an increasingly important role in presenting geographic datasets to end users. Since these datasets can be extremely large, a map rendering system often needs to select a small fraction of the data to visualize them in a limited space. This paper addresses the fundamental challenge of {\em thinning}: determining appropriate samples of data to be shown on specific geographical regions and zoom levels. Other than the sheer scale of the data, the thinning problem is challenging because of a number of other reasons: (1) data can consist of complex geographical shapes, (2) rendering of data needs to satisfy certain constraints, such as data being preserved across zoom levels and adjacent regions, and (3) after satisfying the constraints, an {\em optimal} solution needs to be chosen based on {\em objectives} such as {\em maximality}, {\em fairness}, and {\em importance} of data. This paper formally defines and presents a complete solution to the thinning problem. First, we express the problem as an integer programming formulation that efficiently solves thinning for desired objectives. Second, we present more efficient solutions for maximality, based on DFS traversal of a spatial tree. Third, we consider the common special case of point datasets, and present an even more efficient randomized algorithm. Finally, we have implemented all techniques from this paper in Google Maps visualizations of Fusion Tables, and we describe a set of experiments that demonstrate the tradeoffs among the algorithms. View details
    CloudRAMSort: fast and efficient large-scale distributed RAM sort on shared-nothing cluster
    Changkyu Kim
    Jongsoo Park
    Nadathur Satish
    Pradeep Dubey
    Jatin Chhugani
    Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, ACM, New York, NY, USA, pp. 841-850
    Preview
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