Agnes Axelsson
2024
Proceedings of the 20th Workshop of Young Researchers' Roundtable on Spoken Dialogue Systems
Koji Inoue
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Yahui Fu
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Agnes Axelsson
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Atsumoto Ohashi
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Brielen Madureira
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Yuki Zenimoto
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Biswesh Mohapatra
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Armand Stricker
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Sopan Khosla
Proceedings of the 20th Workshop of Young Researchers' Roundtable on Spoken Dialogue Systems
2023
Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs
Agnes Axelsson
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Gabriel Skantze
Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)
In any system that uses structured knowledge graph (KG) data as its underlying knowledge representation, KG-to-text generation is a useful tool for turning parts of the graph data into text that can be understood by humans. Recent work has shown that models that make use of pretraining on large amounts of text data can perform well on the KG-to-text task, even with relatively little training data on the specific graph-to-text task. In this paper, we build on this concept by using large language models to perform zero-shot generation based on nothing but the model’s understanding of the triple structure from what it can read. We show that ChatGPT achieves near state-of-the-art performance on some measures of the WebNLG 2020 challenge, but falls behind on others. Additionally, we compare factual, counter-factual and fictional statements, and show that there is a significant connection between what the LLM already knows about the data it is parsing and the quality of the output text.
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Co-authors
- Gabriel Skantze 1
- Koji Inoue 1
- Yahui Fu 1
- Atsumoto Ohashi 1
- Brielen Madureira 1
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