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Improving Neural Language Models with Black-Box Analysis and Generalization Through Memorization.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Improving Neural Language Models with Black-Box Analysis and Generalization Through Memorization./
Author:	Khandelwal, Urvashi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	110 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
Subject:	Language. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28671189
ISBN:	9798538199174

Improving Neural Language Models with Black-Box Analysis and Generalization Through Memorization.
Khandelwal, Urvashi.

Improving Neural Language Models with Black-Box Analysis and Generalization Through Memorization. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 110 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Neural language models (LMs) have become the workhorse of most natural language processing tasks and systems today. Yet, they are not perfect, and the two most important challenges in improving them further are (1) their lack of interpretability, and (2) their inability to generalize consistently, both in- and out-of-distribution. In this dissertation, I first describe my work on studying these LMs via black-box analysis, in order to understand how their predictions change in response to strategic changes in inputs. This makes model predictions more transparent by highlighting the features of the input that the model relies on. Then, I describe my work on Generalization through Memorization -- exploiting the notion of similarity between examples by using data saved in an external memory and retrieving nearest neighbors from it. This approach improves existing LM and machine translation models in terms of both in- and out-of-domain generalization, without any added training costs. Beyond improving generalization, memorization also makes model predictions more interpretable.

ISBN: 9798538199174Subjects--Topical Terms:

643551
Language.

Improving Neural Language Models with Black-Box Analysis and Generalization Through Memorization.
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520 $a Neural language models (LMs) have become the workhorse of most natural language processing tasks and systems today. Yet, they are not perfect, and the two most important challenges in improving them further are (1) their lack of interpretability, and (2) their inability to generalize consistently, both in- and out-of-distribution. In this dissertation, I first describe my work on studying these LMs via black-box analysis, in order to understand how their predictions change in response to strategic changes in inputs. This makes model predictions more transparent by highlighting the features of the input that the model relies on. Then, I describe my work on Generalization through Memorization -- exploiting the notion of similarity between examples by using data saved in an external memory and retrieving nearest neighbors from it. This approach improves existing LM and machine translation models in terms of both in- and out-of-domain generalization, without any added training costs. Beyond improving generalization, memorization also makes model predictions more interpretable.
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