User-defined trade-offs in LLM benchmarking: balancing accuracy, scale, and sustainability

• Large language model (LLM) ranking is a problem dependent on the specific use case • Commonly combines both performance and non-performance (often conflicting) criteria • Multi-criteria decision-making offers best practices for solving non-trivial decision-making problems • The PROMETHEE II method offers the desired flexibility and robustness of the LLM ranking process • The LLM ranking process can be made interpretable through visualization techniques This paper presents xLLMBench, a transparent, decision-centric benchmarking framework that empowers decision-makers to rank large language models (LLMs) based on their preferences across diverse, potentially conflicting performance and non-performance criteria, e.g., domain accuracy, model size, energy consumption, CO 2 emissions. Existing LLM benchmarking methods often rely on individual performance criteria (metrics) or human feedback, so methods systematically combining multiple criteria into a single interpretable ranking lack. Methods considering human preferences typically rely on direct human feedback to determine rankings, which can be resource-intensive and not fully aligned with application-specific requirements. Motivated by current limitations of LLM benchmarking, xLLMBench leverages multi-criteria decision-making methods to provide decision-makers with the flexibility to tailor benchmarking processes to their requirements. It focuses on the final step of the benchmarking process (robust analysis of benchmarking results) which in LLMs’ case often involves their ranking. The framework assumes that the selection of datasets, metrics, and LLMs involved in the experiment is conducted following established best practices. We demonstrate xLLMBench’s usefulness in two scenarios: combining LLM results for one metric across different datasets and combining results for multiple metrics within one dataset. Our results show that while some LLMs maintain stable rankings, others exhibit significant changes when correlated datasets are removed, when the focus shifts to contamination-free datasets or fairness metrics. This highlights that LLMs have distinct strengths/weaknesses, going beyond overall performance. Our sensitivity analysis reveals robust rankings, while the diverse visualizations enhance transparency. xLLMBench can be used with existing platforms to support transparent, reproducible, and contextually-meaningful LLM benchmarking.