Abstract
My research focuses on improving textual inference in large language models (LLMs) for natural language generation, particularly in data-to-text generation. While LLMs are increasingly used to generate reports and insights from data, they often produce factually inaccurate or shallow outputs, limiting their usefulness. I work on integrating LLMs with symbolic operations through code generation for deeper and more faithful inferences. As generation tasks are often under-specified, both models and humans rely on implicit presuppositions, and mismatches can lead to errors or misinterpretation. I investigate how such presuppositions affect generation outputs and evaluation, how human presuppositions shape the perceived interestingness of the insights, and how they can be leveraged to improve insight generation.- Anthology ID:
- 2025.ynlg-main.4
- Volume:
[Proceedings of the 1st Workshop for Young Researchers in Natural Language Generation](/volumes/2025.ynlg-main/)- Month:
- October
- Year:
- 2025
- Address:
- Hanoi, Vietnam
- Editors: Alyssa Allen,Nils Feldhus,Rudali Huidrom,Michela Lorandi,Adarsa Sivaprasad,Patrícia Schmidtová- Venue:
[YNLG](/venues/ynlg/)- SIG:
[SIGGEN](/sigs/siggen/)- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 17–20
- Language:
- URL:
[https://aclanthology.org/2025.ynlg-main.4/](https://aclanthology.org/2025.ynlg-main.4/)- DOI:
- Cite (ACL):
- Kristyna Onderkova. 2025. Insight discovery in structured data. InProceedings of the 1st Workshop for Young Researchers in Natural Language Generation, pages 17–20, Hanoi, Vietnam. Association for Computational Linguistics. - Cite (Informal):
[Insight discovery in structured data](https://aclanthology.org/2025.ynlg-main.4/)(Onderkova, YNLG 2025)- PDF:
[https://aclanthology.org/2025.ynlg-main.4.pdf](https://aclanthology.org/2025.ynlg-main.4.pdf)