Sampling Preferences Yields Simple Trustworthiness Scores

• URL: http://arxiv.org/abs/2506.03399v1
• Date: Tue, 03 Jun 2025 21:14:35 GMT
• Title: Sampling Preferences Yields Simple Trustworthiness Scores
• Authors: Sean Steinle,
• Abstract summary: This work introduces preference sampling, a method to extract a scalar trustworthiness score from multi-dimensional evaluation results.<n>We find that preference sampling is consistently reductive, fully reducing the set of candidate models 100% of the time.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the onset of large language models (LLMs), the performance of artificial intelligence (AI) models is becoming increasingly multi-dimensional. Accordingly, there have been several large, multi-dimensional evaluation frameworks put forward to evaluate LLMs. Though these frameworks are much more realistic than previous attempts which only used a single score like accuracy, multi-dimensional evaluations can complicate decision-making since there is no obvious way to select an optimal model. This work introduces preference sampling, a method to extract a scalar trustworthiness score from multi-dimensional evaluation results by considering the many characteristics of model performance which users value. We show that preference sampling improves upon alternate aggregation methods by using multi-dimensional trustworthiness evaluations of LLMs from TrustLLM and DecodingTrust. We find that preference sampling is consistently reductive, fully reducing the set of candidate models 100% of the time whereas Pareto optimality never reduces the set by more than 50%. Likewise, preference sampling is consistently sensitive to user priors-allowing users to specify the relative weighting and confidence of their preferences-whereas averaging scores is intransigent to the users' prior knowledge.

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