How emotions are expressed depends on the context and domain. On X (formerly Twitter), for instance, an author might simply use the hashtag #anger, while in a news headline, emotions are typically written in a more polite, indirect manner. To enable conditional text generation models to create emotionally connotated texts that fit a domain, users need to have access to a parameter that allows them to choose the appropriate way to express an emotion. To achieve this, we introduce MOPO, a Multi-Objective Prompt Optimization methodology. MOPO optimizes prompts according to multiple objectives (which correspond here to the output probabilities assigned by emotion classifiers trained for different domains). In contrast to single objective optimization, MOPO outputs a set of prompts, each with a different weighting of the multiple objectives. Users can then choose the most appropriate prompt for their context. We evaluate MOPO using three objectives, determined by various domain-specific emotion classifiers. MOPO improves performance by up to 15 pp across all objectives with a minimal loss (1-2 pp) for any single objective compared to single-objective optimization. These minor performance losses are offset by a broader generalization across multiple objectives - which is not possible with single-objective optimization. Additionally, MOPO reduces computational requirements by simultaneously optimizing for multiple objectives, eliminating separate optimization procedures for each objective.

翻译：情感表达方式取决于具体语境与领域。例如在X平台（原Twitter）上，作者可能仅使用#愤怒标签，而在新闻标题中情感通常以更礼貌、间接的方式呈现。为使条件文本生成模型能够创作出符合特定领域的情感文本，用户需要能够通过参数选择恰当的情感表达方式。为此，我们提出MOPO——一种多目标提示优化方法。MOPO根据多个目标（此处对应为不同领域训练的情感分类器输出的概率分布）对提示进行优化。与单目标优化不同，MOPO输出一组具有不同目标权重分配的提示集合，用户可根据自身语境选择最合适的提示。我们使用由多个领域专用情感分类器确定的三个目标评估MOPO方法。相较于单目标优化，MOPO在全面提升所有目标性能达15个百分点的同时，对任一单目标的性能损失仅为1-2个百分点。这些微小的性能损失可通过多目标间更广泛的泛化能力得到补偿——这是单目标优化无法实现的优势。此外，MOPO通过同步优化多个目标，避免了为每个目标单独执行优化过程，从而显著降低了计算资源需求。