Emotion-Conditioned Text Generation through Automatic Prompt
  Optimization
Yarik Menchaca Resendiz Roman Klinger
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.