Bias in Evaluating Uncertainty Quantification in Language Models

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Recent Research on Uncertainty Quantification in Language Models

The reliability and safety of language models (LMs) are central aspects for their successful deployment. A key factor here is uncertainty quantification (UQ), which allows for the assessment of the reliability of the outputs generated by LMs. However, recent research shows that common methods for evaluating UQ methods can be influenced by systematic biases, leading to misleading results.

A recently published paper investigates the effects of response length biases on the evaluation of UQ methods. The authors argue that common evaluation metrics, such as the AUROC (Area Under the Receiver Operating Characteristic Curve), are influenced by the length of the generated responses. This bias arises because the correctness functions used to assess the quality of LM outputs themselves exhibit a length dependency. For example, some metrics may rate longer answers as better, regardless of their actual information content.

The study examines seven different correctness functions, including lexical and embedding-based metrics, as well as approaches where another LM acts as an evaluator. These functions were evaluated using four datasets, four different language models, and six UQ methods. The results show that the length biases in the correctness functions distort the evaluation of UQ methods by interacting with the length biases of the UQ methods themselves.

Specifically, this means that UQ methods that tend to classify longer responses as more uncertain can perform better in the evaluation than they actually do. This is because the correctness functions also consider longer answers to be more prone to errors. The authors identify approaches where an LM is used as an evaluator as a promising way to minimize these biases. These approaches are less susceptible to length biases and thus offer a more robust basis for evaluating UQ methods.

The results of this study underscore the importance of carefully selecting evaluation metrics for UQ methods. Considering length biases is crucial to ensure a reliable and meaningful evaluation of the uncertainty of language models. Future research should focus on the development of more robust correctness functions that are less susceptible to length biases and thus enable a more accurate assessment of the actual performance of UQ methods.

For companies like Mindverse, which specialize in the development and implementation of AI solutions, these findings are particularly relevant. The development of reliable and safe AI systems requires a deep understanding of uncertainty quantification and its correct evaluation. Considering the biases highlighted in this study can help improve the quality and reliability of AI applications such as chatbots, voicebots, AI search engines, and knowledge systems.

Bibliographie: http://www.arxiv.org/abs/2504.13677 https://chatpaper.com/chatpaper/paper/130802 https://x.com/gm8xx8/status/1914172589523468733 https://paperreading.club/page?id=300498 https://openreview.net/forum?id=jGtL0JFdeD https://jmlr.org/tmlr/papers/ https://arxiv.org/abs/2503.15850 https://www.auai.org/uai2024/accepted_papers https://openreview.net/pdf?id=jGtL0JFdeD https://openaccess.thecvf.com/content_CVPRW_2020/papers/w1/Ding_Revisiting_the_Evaluation_of_Uncertainty_Estimation_and_Its_Application_to_CVPRW_2020_paper.pdf ```