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Towards Enhanced Language Model Reasoning and Efficient Knowledge Transfer.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Towards Enhanced Language Model Reasoning and Efficient Knowledge Transfer./
Author:	Fang, Yunhao.
Description:	1 online resource (37 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798382827667

Towards Enhanced Language Model Reasoning and Efficient Knowledge Transfer.
Fang, Yunhao.

Towards Enhanced Language Model Reasoning and Efficient Knowledge Transfer. - 1 online resource (37 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--University of California, San Diego, 2024.

Includes bibliographical references

Large language models (LLMs) and vision language models (VLMs) are changing the world and gradually presenting human-level intelligence in various real-world scenarios, including knowledge-based question answering, mathematics, and programming. During the master period, my research focuses on understanding and improving current large language models' reasoning capacity towards general problems solving, and efficient methods to enable the knowledge transfer for vision-language models: distill knowledge from large vision-language models.

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

Mode of access: World Wide Web

ISBN: 9798382827667Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Vision language modelsIndex Terms--Genre/Form:

554714
Electronic books.

Towards Enhanced Language Model Reasoning and Efficient Knowledge Transfer.
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520 $a Large language models (LLMs) and vision language models (VLMs) are changing the world and gradually presenting human-level intelligence in various real-world scenarios, including knowledge-based question answering, mathematics, and programming. During the master period, my research focuses on understanding and improving current large language models' reasoning capacity towards general problems solving, and efficient methods to enable the knowledge transfer for vision-language models: distill knowledge from large vision-language models.
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