Generating Hierarchical JSON Representations of Scientific Sentences Using LLMs
Summary: arXiv:2603.23532v1 Announce Type: cross
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.
Introduction
In the ever-evolving field of artificial intelligence (AI) and natural language processing (NLP), the challenge of accurately representing and reconstructing complex scientific sentences remains a significant area of research. This study explores the application of lightweight language models (LLMs) in generating hierarchical JSON representations of scientific texts, with a focus on preserving the meaning and context of the original sentences.
Methodology
The research employs a fine-tuning approach on a lightweight LLM, utilizing a novel structural loss function specifically designed for this task. The process involves the following steps:
- Data Collection: Sentences are extracted from various scientific articles to create a diverse dataset.
- Model Fine-Tuning: The LLM is fine-tuned on the dataset using the structural loss function, optimizing its ability to generate hierarchical JSON structures.
- JSON Generation: The trained model produces JSON representations for each sentence, capturing the underlying structure and meaning.
- Text Reconstruction: A generative model reconstructs the original text from the JSON structures, allowing for a comparison between original and reconstructed sentences.
Results
The results of this study indicate a promising capability of hierarchical JSON formats in retaining the semantic and lexical integrity of scientific texts. By employing various metrics for comparison, including semantic similarity and lexical analysis, the research demonstrates that the reconstructed sentences closely align with the original content.
Discussion
The findings suggest that structured representations, such as hierarchical JSON, can effectively encapsulate the complexities of scientific language. This has several implications for the fields of AI and NLP, particularly in enhancing the accuracy of information retrieval systems and improving the interpretability of machine-generated texts.
Conclusion
This paper contributes to the ongoing exploration of LLMs in the scientific domain by highlighting the potential of hierarchical JSON representations. Future work may focus on expanding the dataset and refining the structural loss function to further enhance the quality of the generated representations. Overall, this research underscores the viability of structured formats in preserving the essence of scientific communication.
References
For further details, please refer to the full paper available on arXiv: 2603.23532v1.