Workshops

LLM-augmented Preference Learning from Natural Language

Published in 2024 Economics and Computation workshop, 2024

The study focused on using large language models (LLMs) to tackle the scarcity of preference data. By generating preference data and testing various LLMs, the research identified optimal prompts to enhance understanding of preferences in texts. LLama2 was used to condense extensive text, emphasizing preference detection, while BERT’s output, guided by instructive sentences, improved classification accuracy. Techniques like masking and segment embedding were also employed to aid in entity comparison.

Recommended citation: Kang, I., Ruan, S., Ho, T., Lin, J. C., Mohsin, F., Seneviratne, O., & Xia, L. (2023). LLM-augmented Preference Learning from Natural Language. arXiv preprint arXiv:2310.08523.
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Generating Election Data Using Deep Generative Models

Published in AI4SG workshop at AAAI-23, 2023

The study involved generating election data using deep generative models by utilizing strict order complete (SOC) election data, which contains comprehensive information about elections. The SOC election data was converted into images to account for the relationships between the rankings of alternatives. A conditional Deep Convolutional GAN (cDCGAN) was employed for learning, and an edge-trimming penalty was proposed to prevent overfitting or underfitting. Additionally, the quality of the generated data was assessed using an Auto-Encoder reconstruction error method, and a memorization metric was applied to evaluate the occurrence of overfitting

Recommended citation: Lin, Jui Chien, et al. "Generating Election Data Using Deep Generative Models."
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JuiChien (Ryan) Lin

Workshops

LLM-augmented Preference Learning from Natural Language

Generating Election Data Using Deep Generative Models