LLsiM: Large Language Models for Similarity Assessment in Case-Based Reasoning. Lenz, M., Hoffmann, M., & Bergmann, R. In Bichindaritz, I. & López, B., editors, Case-Based Reasoning Research and Development, volume 15662, of Lecture Notes in Computer Science, pages 126–141, Cham, 2025. Springer Nature Switzerland. ![LLsiM: Large Language Models for Similarity Assessment in Case-Based Reasoning [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex 1 download In Case-Based Reasoning (CBR), past experience is used to solve new problems. Determining the most relevant cases is a crucial aspect of this process and is typically based on one or multiple manually-defined similarity measures, requiring deep domain knowledge. To overcome the knowledge-acquisition bottleneck, we propose the use of Large Language Models (LLMs) to automatically assess similarities between cases. We present three distinct approaches where the model is used for different tasks: (i) to predict similarity scores, (ii) to assess pairwise preferences, and (iii) to automatically configure similarity measures. Our conceptual work is accompanied by an open-source Python implementation that we use to evaluate the approaches on three different domains by comparing them to manually crafted similarity measures. Our results show that directly using LLM-based scores does not align well with the baseline rankings, but letting the LLM automatically configure the measures yields rankings that closely resemble the expert-defined ones.
@inproceedings{Lenz2025LLsiMLargeLanguage,
title = {{{LLsiM}}: {{Large Language Models}} for~{{Similarity Assessment}} in~{{Case-Based Reasoning}}},
shorttitle = {{{LLsiM}}},
booktitle = {Case-{{Based Reasoning Research}} and {{Development}}},
author = {Lenz, Mirko and Hoffmann, Maximilian and Bergmann, Ralph},
editor = {Bichindaritz, Isabelle and L{\'o}pez, Beatriz},
year = {2025},
series = {Lecture {{Notes}} in {{Computer Science}}},
volume = {15662},
pages = {126--141},
publisher = {Springer Nature Switzerland},
address = {Cham},
doi = {10.1007/978-3-031-96559-3_9},
abstract = {In Case-Based Reasoning (CBR), past experience is used to solve new problems. Determining the most relevant cases is a crucial aspect of this process and is typically based on one or multiple manually-defined similarity measures, requiring deep domain knowledge. To overcome the knowledge-acquisition bottleneck, we propose the use of Large Language Models (LLMs) to automatically assess similarities between cases. We present three distinct approaches where the model is used for different tasks: (i) to predict similarity scores, (ii) to assess pairwise preferences, and (iii) to automatically configure similarity measures. Our conceptual work is accompanied by an open-source Python implementation that we use to evaluate the approaches on three different domains by comparing them to manually crafted similarity measures. Our results show that directly using LLM-based scores does not align well with the baseline rankings, but letting the LLM automatically configure the measures yields rankings that closely resemble the expert-defined ones.},
isbn = {978-3-031-96559-3},
langid = {english},
url = {https://www.wi2.uni-trier.de/shared/publications/Lenz2025LLsiMLargeLanguage.pdf}
}
Downloads: 1
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