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Bridging Natural Language and Code by Transforming Free-Form Sentences into Sequence of Unambiguous Sentences with Large Language Model

Authors:
Nikita Kiran Yeole
Michael S. Hsiao

Keywords: Natural language programming; decomposition; chain-of-thought reasoning.

Abstract:
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 proposed solution is a Large language model 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 percent of the free-form sentences containing these problems were successfully converted to sequences of simple unambiguous object-oriented sentences by our approach.

Pages: 4 to 10

Copyright: Copyright (c) IARIA, 2024

Publication date: May 26, 2024

Published in: conference

ISSN: 2308-4367

ISBN: 978-1-68558-166-4

Location: Barcelona, Spain

Dates: from May 26, 2024 to May 30, 2024