1 citations · 1 across the 5 of their papers we have counts for
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Prompting Implicit Discourse Relation Annotation
Frances Yung, Mansoor Ahmad, Merel Scholman +1
Pre-trained large language models, such as ChatGPT, archive outstanding performance in various reasoning tasks without supervised training and were found to have outperformed crowd…
Do large language models and humans have similar behaviors in causal inference with script knowledge?
Xudong Hong, Margarita Ryzhova, Daniel Adrian Biondi +1
Recently, large pre-trained language models (LLMs) have demonstrated superior language understanding abilities, including zero-shot causal reasoning. However, it is unclear to what…
Tackling Hallucinations in Neural Chart Summarization
Saad Obaid ul Islam, Iza Škrjanec, Ondřej Dušek +1
Hallucinations in text generation occur when the system produces text that is not grounded in the input. In this work, we tackle the problem of hallucinations in neural chart summa…
Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design
Valentina Pyatkin, Frances Yung, Merel C. J. Scholman +3
Disagreement in natural language annotation has mostly been studied from a perspective of biases introduced by the annotators and the annotation frameworks. Here, we propose to ana…
Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences
Xudong Hong, Asad Sayeed, Khushboo Mehra +2
Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story gene…