Abstract:
Current research state-of-the-art in automatic data-to-text generation, a major task in natural language generation, is dominated by large language models based on the Transformer neural network architecture. These models are capable of producing lifelike, natural texts; however, they are hard to control and often do not adhere to the input data, omitting important content or producing “hallucinated” text which is not grounded in the input data. In this talk, I will first show the basic operation principles of the large language models. I will then detail our experiments aiming at higher accuracy of generated text in two ways: (1) improving accuracy of the generating language models themselves, (2) automatically detecting errors in generated texts.
Bio:
Ondřej Dušek is an assistant professor at the Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University. His research is in the areas of natural language generation and dialogue…
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