ملف الباحث

Saurabh Tiwary

6 أوراق في مجموعة PaperMetrix

المنشورات

أوراق هذا المؤلف

  1. Leading Conversational Search by Suggesting Useful Questions

    2020

    This paper studies a new scenario in conversational search, conversational question suggestion, which leads search engine users to more engaging experiences by suggesting interesting, informative, and useful follow-up questions. We first establish a novel evaluation …

  2. METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals

    2022 · arXiv (Cornell University)

    We present an efficient method of pretraining large-scale autoencoding language models using training signals generated by an auxiliary model. Originated in ELECTRA, this training strategy has demonstrated sample-efficiency to pretrain models at the scale of …

  3. MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

    2016 · Neural Information Processing Systems

    This paper presents our recent work on the design and development of a new, large scale dataset, which we name MS MARCO, for MAchine Reading COmprehension. This new dataset is aimed to overcome a number …

  4. Neural Ranking Models with Multiple Document Fields

    2018

    Deep neural networks have recently shown promise in the ad-hoc retrieval task. However, such models have often been based on one field of the document, for example considering document title only or document body only. …

  5. Analysis of Points of Interests Recommended for Leisure Walk Descriptions

    2024 · arXiv (Cornell University)

    Data for Sub-Task 1 of the Advertisement in Retrieval-Augmented Generation task at Touché 2025. The dataset contains segments retrieved from the segmented version of MS MARCO V2.1. The queries used in retrieval are taken from …

  6. Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

    2022 · arXiv (Cornell University)

    Pretrained general-purpose language models can achieve state-of-the-art accuracies in various natural language processing domains by adapting to downstream tasks via zero-shot, few-shot and fine-tuning techniques. Because of their success, the size of these models has …