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Towards Augmenting and Evaluating La...

Liu, Tianyang.

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Towards Augmenting and Evaluating Large Language Models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards Augmenting and Evaluating Large Language Models./
Author:	Liu, Tianyang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	98 p.
Notes:	Source: Masters Abstracts International, Volume: 85-10.
Contained By:	Masters Abstracts International85-10.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30993449
ISBN:	9798381978063

Towards Augmenting and Evaluating Large Language Models.
Liu, Tianyang.

Towards Augmenting and Evaluating Large Language Models. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 98 p.

Source: Masters Abstracts International, Volume: 85-10.

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

In the rapidly evolving field of Natural Language Processing (NLP), the advent of Large Language Models (LLMs) marks a significant milestone, setting new standards in language understanding and generation. This thesis focuses on augmenting and evaluating LLMs, introducing ToolkenGPT, a novel method to integrate external tools via tool embeddings to enrich model functionality and adaptability and RepoBench, a benchmark for assessing the proficiency of LLMs in handling repository-level code auto-completion. Additionally, this thesis rethinks approaches towards tabular data reasoning, exploring how LLMs can be better tailored to understand and interpret structured data formats effectively.

ISBN: 9798381978063Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Large Language Models

Towards Augmenting and Evaluating Large Language Models.
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245 1 0 $a Towards Augmenting and Evaluating Large Language Models.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 98 p.
500 $a Source: Masters Abstracts International, Volume: 85-10.
500 $a Advisor: McAuley, Julian.
502 $a Thesis (M.S.)--University of California, San Diego, 2024.
520 $a In the rapidly evolving field of Natural Language Processing (NLP), the advent of Large Language Models (LLMs) marks a significant milestone, setting new standards in language understanding and generation. This thesis focuses on augmenting and evaluating LLMs, introducing ToolkenGPT, a novel method to integrate external tools via tool embeddings to enrich model functionality and adaptability and RepoBench, a benchmark for assessing the proficiency of LLMs in handling repository-level code auto-completion. Additionally, this thesis rethinks approaches towards tabular data reasoning, exploring how LLMs can be better tailored to understand and interpret structured data formats effectively.
590 $a School code: 0033.
650 4 $a Computer science. $3 523869
650 4 $a Computer engineering. $3 621879
653 $a Large Language Models
653 $a Natural Language Processing
653 $a Tabular data reasoning
653 $a Auto-completion
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690 $a 0464
710 2 $a University of California, San Diego. $b Computer Science and Engineering. $3 1018473
773 0 $t Masters Abstracts International $g 85-10.
790 $a 0033
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792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30993449