4 papers
PARL: Prompt-based Agents for Reinforcement Learning
Yarik Menchaca Resendiz, Roman Klinger
Large language models (LLMs) have demonstrated high performance on tasks expressed in natural language, particularly in zero- or few-shot settings. These are typically framed as su…
iPrOp: Interactive Prompt Optimization for Large Language Models with a Human in the Loop
Jiahui Li, Roman Klinger
Prompt engineering has made significant contributions to the era of large language models, yet its effectiveness depends on the skills of a prompt author. This paper introduces $\t…
LLM-based Affective Text Generation Quality Based on Different Quantization Values
Yarik Menchaca Resendiz, Roman Klinger
Large language models exhibit a remarkable capacity in language generation and comprehension. These advances enable AI systems to produce more human-like and emotionally engaging t…
MOPO: Multi-Objective Prompt Optimization for Affective Text Generation
Yarik Menchaca Resendiz, Roman Klinger
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, emotio…