299 citations · 531 across the 12 of their papers we have counts for
15 papers
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Ying-Chun Lin, Jennifer Neville, Jack W. Stokes +14
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their sati…
GenSERP: Large Language Models for Whole Page Presentation
Zhenning Zhang, Yunan Zhang, Suyu Ge +4
The advent of large language models (LLMs) brings an opportunity to minimize the effort in search engine result page (SERP) organization. In this paper, we propose GenSERP, a frame…
DUBLIN -- Document Understanding By Language-Image Network
Kriti Aggarwal, Aditi Khandelwal, Kumar Tanmay +7
Visual document understanding is a complex task that involves analyzing both the text and the visual elements in document images. Existing models often rely on manual feature engin…
METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals
Payal Bajaj, Chenyan Xiong, Guolin Ke +7
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…
Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
Yu Meng, Chenyan Xiong, Payal Bajaj +4
We present a new framework AMOS that pretrains text encoders with an Adversarial learning curriculum via a Mixture Of Signals from multiple auxiliary generators. Following ELECTRA-…
Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
Shaden Smith, Mostofa Patwary, Brandon Norick +17
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…
COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
Yu Meng, Chenyan Xiong, Payal Bajaj +4
We present a self-supervised learning framework, COCO-LM, that pretrains Language Models by COrrecting and COntrasting corrupted text sequences. Following ELECTRA-style pretraining…
Knowledge-Aware Language Model Pretraining
Corby Rosset, Chenyan Xiong, Minh Phan +3
How much knowledge do pretrained language models hold? Recent research observed that pretrained transformers are adept at modeling semantics but it is unclear to what degree they g…
Generic Intent Representation in Web Search
Hongfei Zhang, Xia Song, Chenyan Xiong +4
This paper presents GEneric iNtent Encoder (GEN Encoder) which learns a distributed representation space for user intent in search. Leveraging large scale user clicks from Bing sea…
An Axiomatic Approach to Regularizing Neural Ranking Models
Corby Rosset, Bhaskar Mitra, Chenyan Xiong +3
Axiomatic information retrieval (IR) seeks a set of principle properties desirable in IR models. These properties when formally expressed provide guidance in the search for better…
Towards Language Agnostic Universal Representations
Armen Aghajanyan, Xia Song, Saurabh Tiwary
When a bilingual student learns to solve word problems in math, we expect the student to be able to solve these problem in both languages the student is fluent in,even if the math…
Optimizing Query Evaluations using Reinforcement Learning for Web Search
Corby Rosset, Damien Jose, Gargi Ghosh +2
In web search, typically a candidate generation step selects a small set of documents---from collections containing as many as billions of web pages---that are subsequently ranked…
Neural Ranking Models with Multiple Document Fields
Hamed Zamani, Bhaskar Mitra, Xia Song +2
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 docu…
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Payal Bajaj, Daniel Campos, Nick Craswell +12
We introduce a large scale MAchine Reading COmprehension dataset, which we name MS MARCO. The dataset comprises of 1,010,916 anonymized questions---sampled from Bing's search query…
Generating Binary Optimal Codes Using Heterogeneous Parallel Computing
Srajan Paliwal, Saurabh Tiwary, Bhaskar Chaudhury +1
Generation of optimal codes is a well known problem in coding theory. Many computational approaches exist in the literature for finding record breaking codes. However generating co…