國立虎尾科技大學 |

User-Centric Natural Language Processing.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	User-Centric Natural Language Processing./
Author:	Majumder, Bodhisattwa Prasad.
Description:	1 online resource (186 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798379760663

User-Centric Natural Language Processing.
Majumder, Bodhisattwa Prasad.

User-Centric Natural Language Processing. - 1 online resource (186 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2023.

Includes bibliographical references

Artificial Intelligence (AI) systems that use language generation models hold incredible promise to assist humans to perform complex decision-making tasks. State-of-the-art language generation models can produce engaging content, reason about the world, and retrieve relevant information for an information-seeking task. However, these models often ignore sparse, long-tail knowledge about individual users, cultural subtleties, and domain-specific knowledge, preventing end users from reaping the full benefit of the scale. In this dissertation, we redesign AI systems to start with individual needs.Ideally, a user-centric AI system must be grounded in the real-world, produce faithful chains of reasoning to explain its prediction, and align with the user's preferences. We elevate existing AI systems with knowledge, explanations, and interactions and develop both training-time and post-hoc techniques to make these systems user-centric. We show additional knowledge-grounding promotes user success in achieving conversational goals while using a conversational AI system. We demonstrate that AI explanations, when attributed to world knowledge, render them to be faithful and consistent. Finally, we discover that user-centric interventionist approach can help users obtain more equitable predictions backed by faithful explanations as compared to a black-box counterpart. In summary, our research establishes that increased effectiveness, explainability, and equitability can be achieved through knowledge-grounding and user-centric approaches to personalize AI models-a long-standing goal of artificial general intelligence.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379760663Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Dialog systemsIndex Terms--Genre/Form:

554714
Electronic books.

User-Centric Natural Language Processing.
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520 $a Artificial Intelligence (AI) systems that use language generation models hold incredible promise to assist humans to perform complex decision-making tasks. State-of-the-art language generation models can produce engaging content, reason about the world, and retrieve relevant information for an information-seeking task. However, these models often ignore sparse, long-tail knowledge about individual users, cultural subtleties, and domain-specific knowledge, preventing end users from reaping the full benefit of the scale. In this dissertation, we redesign AI systems to start with individual needs.Ideally, a user-centric AI system must be grounded in the real-world, produce faithful chains of reasoning to explain its prediction, and align with the user's preferences. We elevate existing AI systems with knowledge, explanations, and interactions and develop both training-time and post-hoc techniques to make these systems user-centric. We show additional knowledge-grounding promotes user success in achieving conversational goals while using a conversational AI system. We demonstrate that AI explanations, when attributed to world knowledge, render them to be faithful and consistent. Finally, we discover that user-centric interventionist approach can help users obtain more equitable predictions backed by faithful explanations as compared to a black-box counterpart. In summary, our research establishes that increased effectiveness, explainability, and equitability can be achieved through knowledge-grounding and user-centric approaches to personalize AI models-a long-standing goal of artificial general intelligence.
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