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Advancing Mathematical Reasoning With Language Models : = A Multimodal and Knowledge-Intensive Perspective.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Advancing Mathematical Reasoning With Language Models :/
Reminder of title:	A Multimodal and Knowledge-Intensive Perspective.
Author:	Lu, Pan.
Description:	1 online resource (208 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: A.
Contained By:	Dissertations Abstracts International85-11A.
Subject:	Linguistics. -
Online resource:	click for full text (PQDT)
ISBN:	9798382747545

Advancing Mathematical Reasoning With Language Models : = A Multimodal and Knowledge-Intensive Perspective.
Lu, Pan.

Advancing Mathematical Reasoning With Language Models :A Multimodal and Knowledge-Intensive Perspective. - 1 online resource (208 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: A.

Thesis (Ph.D.)--University of California, Los Angeles, 2024.

Includes bibliographical references

Mathematical reasoning is a pivotal component of human intelligence, crucial for advancing education and science. This dissertation delves into the development of language model systems capable of robust mathematical reasoning, marking a significant step toward realizing general artificial intelligence. We introduce multi-modal and knowledge-intensive benchmarks to assess the reasoning capabilities of large language models (LLMs) and vision-language models (VLMs) across real-world contexts, including visual information, tabular data, and scientific domains.This dissertation advances the field by proposing new pre-trained VLMs. For instance, Patch-Trm introduces a patch-based cross-modal Transformer model for abstract diagram reasoning. We also present innovative retrieval and tool-augmented algorithms that enhance LLM capabilities. Notably, Inter-GPS is a neuro-symbolic solver for geometry that demonstrates human-level performance, marking a first in the domain. Additionally, PromptPG pioneers the use of reinforcement learning for dynamic in-context example selection, significantly improving the stability and accuracy of LLMs. Another groundbreaking contribution is Chameleon, a model that integrates LLMs with external tools, vastly increasing their flexibility and effectiveness in real-world applications. The dissertation concludes by analyzing the latest advances in mathematical reasoning within visual contexts, and highlighting the current challenges and future prospects.

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

Mode of access: World Wide Web

ISBN: 9798382747545Subjects--Topical Terms:

557829
Linguistics.
Subjects--Index Terms:

Language modelsIndex Terms--Genre/Form:

554714
Electronic books.

Advancing Mathematical Reasoning With Language Models : = A Multimodal and Knowledge-Intensive Perspective.
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520 $a Mathematical reasoning is a pivotal component of human intelligence, crucial for advancing education and science. This dissertation delves into the development of language model systems capable of robust mathematical reasoning, marking a significant step toward realizing general artificial intelligence. We introduce multi-modal and knowledge-intensive benchmarks to assess the reasoning capabilities of large language models (LLMs) and vision-language models (VLMs) across real-world contexts, including visual information, tabular data, and scientific domains.This dissertation advances the field by proposing new pre-trained VLMs. For instance, Patch-Trm introduces a patch-based cross-modal Transformer model for abstract diagram reasoning. We also present innovative retrieval and tool-augmented algorithms that enhance LLM capabilities. Notably, Inter-GPS is a neuro-symbolic solver for geometry that demonstrates human-level performance, marking a first in the domain. Additionally, PromptPG pioneers the use of reinforcement learning for dynamic in-context example selection, significantly improving the stability and accuracy of LLMs. Another groundbreaking contribution is Chameleon, a model that integrates LLMs with external tools, vastly increasing their flexibility and effectiveness in real-world applications. The dissertation concludes by analyzing the latest advances in mathematical reasoning within visual contexts, and highlighting the current challenges and future prospects.
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