Sampling Preferences Yields Simple Trustworthiness Scores
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909636620189696 |
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| author | Steinle, Sean |
| author_facet | Steinle, Sean |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03399 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sampling Preferences Yields Simple Trustworthiness Scores Steinle, Sean Human-Computer Interaction Artificial Intelligence 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. |
| title | Sampling Preferences Yields Simple Trustworthiness Scores |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2506.03399 |