# MOPO: Multi-Objective Prompt Optimization for Affective Text Generation

arXiv: https://arxiv.org/abs/2412.12948 · HTML: https://mlsanity.com/paper/2412.12948/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Builds on (most cited first, 22 of 22)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://mlsanity.com/paper/2309.16797/) (2023)
- [RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://mlsanity.com/paper/2205.12548/) (2022)
- [Few-shot Learning with Multilingual Language Models](https://mlsanity.com/paper/2112.10668/) (2021)
- [Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification](https://mlsanity.com/paper/2108.02035/) (2021)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [OpenPrompt: An Open-source Framework for Prompt-learning](https://mlsanity.com/paper/2111.01998/) (2021)
- [Planning with Learned Entity Prompts for Abstractive Summarization](https://mlsanity.com/paper/2104.07606/) (2021)
- [WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia](https://mlsanity.com/paper/2305.14292/) (2023)
- [Polite Dialogue Generation Without Parallel Data](https://mlsanity.com/paper/1805.03162/) (2018)
- [Affect-LM: A Neural Language Model for Customizable Affective Text Generation](https://mlsanity.com/paper/1704.06851/) (2017)
- [Affect-Driven Dialog Generation](https://mlsanity.com/paper/1904.02793/) (2019)
- [CoCon: A Self-Supervised Approach for Controlled Text Generation](https://mlsanity.com/paper/2006.03535/) (2020)
- [Towards Efficient NLP: A Standard Evaluation and A Strong Baseline](https://mlsanity.com/paper/2110.07038/) (2021)
- [Towards Higher Pareto Frontier in Multilingual Machine Translation](https://mlsanity.com/paper/2305.15718/) (2023)
- [Facts2Story: Controlling Text Generation by Key Facts](https://mlsanity.com/paper/2012.04332/) (2020)
- [Emotion-Conditioned Text Generation through Automatic Prompt Optimization](https://mlsanity.com/paper/2308.04857/) (2023)
- [Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluation](https://mlsanity.com/paper/2305.19330/) (2023)
- [Affective Natural Language Generation of Event Descriptions through Fine-grained Appraisal Conditions](https://mlsanity.com/paper/2307.14004/) (2023)

## Built on by (most cited first, 9 of 9)

- [A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm](https://mlsanity.com/paper/2502.18746/) (2025)
- [PromptSuite: A Task-Agnostic Framework for Multi-Prompt Generation](https://mlsanity.com/paper/2507.14913/) (2025)
- [When Gradients Collide: Failure Modes of Multi-Objective Prompt Optimization for LLM Judges](https://mlsanity.com/paper/2605.26046/) (2026)
- [Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits](https://mlsanity.com/paper/2605.14553/) (2026)
- [MO-CAPO: Multi-Objective Cost-Aware Prompt Optimization](https://mlsanity.com/paper/2605.18869/) (2026)
- [CROP: Token-Efficient Reasoning in Large Language Models via Regularized Prompt Optimization](https://mlsanity.com/paper/2604.14214/) (2026)
- [CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts](https://mlsanity.com/paper/2606.04661/) (2026)
- [CAPO: Constraint-Aware Prompt Optimization for LLM Agents](https://mlsanity.com/paper/2608.16068/) (2026)
- [Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study](https://mlsanity.com/paper/2610.13287/) (2026)

## Abstract (arXiv)

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.