Transforming Free-Form Sentences into Sequence of Unambiguous Sentences with Large Language Model

In the realm of natural language programming, translating free-form sentences in natural language into a functional, machine-executable program remains difficult due to the following 4 challenges. First, the inherent ambiguity of natural languages. Second, the high-level verbose nature in user descriptions. Third, the complexity in the sentences and Fourth, the invalid or semantically unclear sentences. Our first solution is a Large Language Model (LLM) based Artificial Intelligence driven assistant to process free-form sentences and decompose them into sequences of simplified, unambiguous sentences that abide by a set of rules, thereby stripping away the complexities embedded within the original sentences. These resulting sentences are then used to generate the code. We applied the proposed approach to a set of free-form sentences written by middle-school students for describing the logic behind video games. More than 60% of the free-form sentences containing these problems were sufficiently converted to sequences of simple unambiguous object-oriented sentences by our approach. Next, the thesis also presents "IntentGuide," a neuro-symbolic integration framework to enhance the clarity and executability of human intentions expressed in freeform sentences. IntentGuide effectively integrates the rule-based error detection capabilities of symbolic AI with the powerful adaptive learning abilities of Large Language Model to convert ambiguous or complex sentences into clear, machine-understandable instructions. The empirical evaluation of IntentGuide performed on natural language sentences written by middle school students for designing video games, reveals a significant improvement in error correction and code generation abilities compared to previous approach, attaining an accuracy rate of 90%.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and#38; Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Yeole, Nikita Kiran

Chairs dc:contributor.committeechair

• Huang, Lifu
• Hsiao, Michael S.

Committee member dc:contributor.committeemember

• Viswanath, Bimal

• Natural language programming
• decomposition
• Natural language processing
• neuro-symbolic

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:42030

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/123829