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    <abstract>Large Language Models (LLMs) have significantly altered the landscape of Natural Language Processing (NLP), having topped the benchmarks of many standard tasks and problems, particularly when used in combination with Retrieval Augmented Generation (RAG). Despite their impressive performance and relative simplicity, its use as a baseline method has not been extensive. One of the reasons might be that adapting and optimizing RAG-based pipelines for specific NLP tasks generally requires custom development which is difficult to scale. In this work we introduce RAGthoven, a tool for automatic evaluation of RAG-based pipelines. It provides a simple yet powerful abstraction, which allows the user to start the evaluation process with nothing more than a single configuration file. To demonstrate its usefulness we conduct three case studies spanning text classification, question answering and code generation usecases. We release the code, as well as the documentation and tutorials, at https://github.com/ragthoven-dev/ragthoven</abstract>
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%0 Conference Proceedings
%T RAGthoven: A Configurable Toolkit for RAG-enabled LLM Experimentation
%A Karetka, Gregor
%A Skottis, Demetris
%A Dutková, Lucia
%A Hraška, Peter
%A Suppa, Marek
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Mather, Brodie
%Y Dras, Mark
%S Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F karetka-etal-2025-ragthoven
%X Large Language Models (LLMs) have significantly altered the landscape of Natural Language Processing (NLP), having topped the benchmarks of many standard tasks and problems, particularly when used in combination with Retrieval Augmented Generation (RAG). Despite their impressive performance and relative simplicity, its use as a baseline method has not been extensive. One of the reasons might be that adapting and optimizing RAG-based pipelines for specific NLP tasks generally requires custom development which is difficult to scale. In this work we introduce RAGthoven, a tool for automatic evaluation of RAG-based pipelines. It provides a simple yet powerful abstraction, which allows the user to start the evaluation process with nothing more than a single configuration file. To demonstrate its usefulness we conduct three case studies spanning text classification, question answering and code generation usecases. We release the code, as well as the documentation and tutorials, at https://github.com/ragthoven-dev/ragthoven
%U https://aclanthology.org/2025.coling-demos.12/
%P 117-125

Markdown (Informal)

[RAGthoven: A Configurable Toolkit for RAG-enabled LLM Experimentation](https://aclanthology.org/2025.coling-demos.12/) (Karetka et al., COLING 2025)

• RAGthoven: A Configurable Toolkit for RAG-enabled LLM Experimentation (Karetka et al., COLING 2025)

ACL

• Gregor Karetka, Demetris Skottis, Lucia Dutková, Peter Hraška, and Marek Suppa. 2025. RAGthoven: A Configurable Toolkit for RAG-enabled LLM Experimentation. In Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations, pages 117–125, Abu Dhabi, UAE. Association for Computational Linguistics.