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Found 6 papers, 5 papers with code
Does GPT-3 Generate Empathetic Dialogues? A Novel In-Context Example Selection Method and Automatic Evaluation Metric for Empathetic Dialogue Generation
Although several studies have investigated few-shot in-context learning for empathetic dialogue generation, an in-depth analysis of the generation of empathetic dialogue with in-context learning remains unclear, especially in GPT-3 (Brown et al., 2020).
PERSONACHATGEN: Generating Personalized Dialogues using GPT-3
However, since this dataset is frozen in 2018, the dialogue agents trained on this dataset would not know how to interact with a human who loves “Wandavision.” One way to alleviate this problem is to create a large-scale dataset.
Large Language Models can Share Images, Too!
To our knowledge, this is the first study to assess the image-sharing ability of LLMs in a zero-shot setting without visual foundation models.
DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset
Through this pipeline, we introduce DialogCC, a high-quality and diverse multi-modal dialogue dataset that surpasses existing datasets in terms of quality and diversity in human evaluation.
Korean-Specific Emotion Annotation Procedure Using N-Gram-Based Distant Supervision and Korean-Specific-Feature-Based Distant Supervision
In order to construct our dataset, we used a large-scale sentiment movie review corpus as the unlabeled dataset.
Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks
This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their sound data.
