Papers with evolutionary algorithms
Mergenetic: a Simple Evolutionary Model Merging Library (2025.acl-demo)
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| Challenge: | Recent work shows that combining model merging with evolutionary algorithms can boost performance, but there is currently no library for experimenting with different evolutionary algorithms and merging methods. |
| Approach: | They propose an open-source library for evolutionary model merging that enables easy composition of merging methods and evolutionary algorithms while incorporating lightweight fitness estimators to reduce evaluation costs. |
| Outcome: | The proposed library produces competitive results across languages and tasks using modest hardware. |
ITERATE: Image-Text Enhancement, Retrieval, and Alignment for Transmodal Evolution with LLMs (2025.coling-main)
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| Challenge: | a new framework for visual annotation of text-based questions is needed to improve performance . obtaining corresponding images through manual annotation often entails high costs . |
| Approach: | They propose a framework that uses visual modality to enhance the performance of text-based questions. |
| Outcome: | The proposed framework improves the alignment between text and images by using search engines or web scraping techniques. |
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. |
Knowledge Fusion By Evolving Weights of Language Models (2024.findings-acl)
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| Challenge: | Experimental results on mainstream language models show that Evolver outperforms previous state-of-the-art models by large margins due to the high training costs of large language models. |
| Approach: | They propose a method to integrate multiple models from diverse training scenarios into a unified model. |
| Outcome: | The proposed method outperforms state-of-the-art models on mainstream language models by large margins. |