Papers by Niklas Herbster
GenDLN: Evolutionary Algorithm-Based Stacked LLM Framework for Joint Prompt Optimization (2025.acl-srw)
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| Challenge: | Large Language Models (LLMs) are increasingly replacing traditional classification and inference models due to their generality, ability to perform a wide range of tasks, and seemingly advanced "reasoning" prompt optimization is a promising alternative to manual/human prompt engineering, but the cost of using LLMs for prompt optimization via commercial APIs remains high. |
| Approach: | They propose an open-source, efficient genetic algorithm-based prompt pair optimization framework that leverages commercial APIs. |
| Outcome: | The proposed approach allows teams with limited resources to efficiently use commercial LLMs for prompt optimization. |