@inproceedings{resendiz-klinger-2023-emotion,
title = "Emotion-Conditioned Text Generation through Automatic Prompt Optimization",
author = "Resendiz, Yarik Menchaca and
Klinger, Roman",
editor = "Hazarika, Devamanyu and
Tang, Xiangru Robert and
Jin, Di",
booktitle = "Proceedings of the 1st Workshop on Taming Large Language Models: Controllability in the era of Interactive Assistants!",
month = sep,
year = "2023",
address = "Prague, Czech Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.tllm-1.3",
pages = "24--30",
abstract = "Conditional natural language generation methods often require either expensive fine-tuning or training a large language model from scratch. Both are unlikely to lead to good results without a substantial amount of data and computational resources. Prompt learning without changing the parameters of a large language model presents a promising alternative. It is a cost-effective approach, while still achieving competitive results. While this procedure is now established for zero- and few-shot text classification and structured prediction, it has received limited attention in conditional text generation. We present the first automatic prompt optimization approach for emotion-conditioned text generation with instruction-fine-tuned models. Our method uses an iterative optimization procedure that changes the prompt by adding, removing, or replacing tokens. As objective function, we only require a text classifier that measures the realization of the conditional variable in the generated text. We evaluate the method on emotion-conditioned text generation with a focus on event reports and compare it to manually designed prompts that also act as the seed for the optimization procedure. The optimized prompts achieve 0.75 macro-average F1 to fulfill the emotion condition in contrast to manually designed seed prompts with only 0.22 macro-average F1.",
}
Markdown (Informal)
[Emotion-Conditioned Text Generation through Automatic Prompt Optimization](https://aclanthology.org/2023.tllm-1.3) (Resendiz & Klinger, TLLM-WS 2023)
ACL