Abstract
Neural Language Models have become central to Natural Language Generation (NLG) research and can produce fluent and coherent text. However, when models generate text from complex, structured or long-form data such as tables or event logs, they often hallucinate and introduce factual errors. These hallucinations limit the practical deployment of large language models (LLMs) in applications where factual accuracy is critical. In my research, factual accuracy refers to the faithfulness of the generated text to the given input data. My PhD focuses on reducing hallucinations and improving factual accuracy in data-to-text generation, which I address through two core approaches: (i) analysing how input quality and structure improve factual accuracy, and (ii) developing a manual error annotation protocol and extending it into an LLM-as-Judge framework. This work aims to assess when automatic evaluation can complement human annotation and enable larger-scale evaluation.- Anthology ID:
- 2025.ynlg-main.3
- 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:
- 10–16
- Language:
- URL:
[https://aclanthology.org/2025.ynlg-main.3/](https://aclanthology.org/2025.ynlg-main.3/)- DOI:
- Cite (ACL):
- Barkavi Sundararajan. 2025. Improving Factual Accuracy in Neural Data-to-Text Generation through Input Quality and Scalable Evaluation. InProceedings of the 1st Workshop for Young Researchers in Natural Language Generation, pages 10–16, Hanoi, Vietnam. Association for Computational Linguistics. - Cite (Informal):
[Improving Factual Accuracy in Neural Data-to-Text Generation through Input Quality and Scalable Evaluation](https://aclanthology.org/2025.ynlg-main.3/)(Sundararajan, YNLG 2025)- PDF:
[https://aclanthology.org/2025.ynlg-main.3.pdf](https://aclanthology.org/2025.ynlg-main.3.pdf)