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Towards Augmenting and Evaluating Large Language Models.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Towards Augmenting and Evaluating Large Language Models./
Author:	Liu, Tianyang.
Description:	1 online resource (98 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-10.
Contained By:	Masters Abstracts International85-10.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798381978063

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

Towards Augmenting and Evaluating Large Language Models. - 1 online resource (98 pages)

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

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

Includes bibliographical references

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.

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

Mode of access: World Wide Web

ISBN: 9798381978063Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Large Language ModelsIndex Terms--Genre/Form:

554714
Electronic books.

Towards Augmenting and Evaluating Large Language Models.
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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.
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