Papers by Diego Klabjan
PromptFE: Automated Feature Engineering by Prompting (2026.eacl-long)
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| Challenge: | Existing approaches to feature engineering relied on domain expertise to build features. |
| Approach: | They propose a framework that leverages large language models to automatically construct features in a string format and generate semantic explanations based on dataset descriptions. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on real-world datasets. |
SOPL: A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models (2025.findings-emnlp)
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| Challenge: | Using automated prompt engineering to identify effective features is essential for large language models. |
| Approach: | They propose an optimal learning framework for automated prompt engineering for black-box models . feature-based method is used to express prompt templates, which broadens the search space . |
| Outcome: | The proposed learning framework outperforms benchmark strategies on instruction induction tasks with limited budgets. |
Zero-shot Graph Reasoning via Retrieval Augmented Framework with LLMs (2025.findings-emnlp)
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| Challenge: | Existing methods that require extensive finetuning or depend on predefined algorithms are limited by training. |
| Approach: | a new retrieval-augmented framework is proposed that harnesses retrieval and large language models to address graph reasoning tasks. |
| Outcome: | The proposed method achieves 100% accuracy on most graph reasoning tasks while maintaining consistent token costs regardless of graph sizes. |
Reverse Prompt Engineering: A Zero-Shot, Genetic Algorithm Approach to Language Model Inversion (2025.emnlp-main)
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| Challenge: | Existing methods rely on a large number of outputs for training and inference, and they can produce garbled text. |
| Approach: | They propose a training-free framework that reconstructs prompts using only a limited number of text outputs from a language model. |
| Outcome: | The proposed framework generates high-quality prompt recovery and more semantically and functionally aligned with the originals than current state-of-the-art methods. |