Generating Hierarchical JSON Representations of Scientific Sentences Using LLMs

Computer Science > Computation and Language arXiv:2603.23532 (cs) [Submitted on 8 Mar 2026] Title:Generating Hierarchical JSON Representations of Scientific Sentences Using LLMs Authors:Satya Sri Rajiteswari Nimmagadda, Ethan Young, Niladri Sengupta, Ananya Jana, Aniruddha Maiti View a PDF of the paper titled Generating Hierarchical JSON Representations of Scientific Sentences Using LLMs, by Satya Sri Rajiteswari Nimmagadda and 4 other authors View PDF HTML (experimental)

Abstract:This paper investigates whether structured representations can preserve the meaning of scientific sentences. To test this, a lightweight LLM is fine-tuned using a novel structural loss function to generate hierarchical JSON structures from sentences collected from scientific articles. These JSONs are then used by a generative model to reconstruct the original text. Comparing the original and reconstructed sentences using semantic and lexical similarity we show that hierarchical formats are capable of retaining information of scientific texts effectively.

Comments:	accepted to 21th International Conference on Semantic Computing (IEEE ICSC 2026)
Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2603.23532 [cs.CL]
	(or arXiv:2603.23532v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2603.23532 Focus to learn more arXiv-issued DOI via DataCite

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From: Ananya Jana [view email]
[v1] Sun, 8 Mar 2026 00:12:44 UTC (12 KB)
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