Revisiting Uncertainty Quantification Evaluation in Language Models:
  Spurious Interactions with Response Length Bias Results
Uncertainty Quantification (UQ) in Language Models (LMs) is crucial for
improving their safety and reliability. Evaluations often use performance
metrics like AUROC to assess how well UQ methods (e.g., negative sequence
probabilities) correlate with task correctness functions (e.g., ROUGE-L). In
this paper, we show that commonly used correctness functions bias UQ
evaluations by inflating the performance of certain UQ methods. We evaluate 7
correctness functions -- from lexical-based and embedding-based metrics to
LLM-as-a-judge approaches -- across 4 datasets x 4 models x 6 UQ methods. Our
analysis reveals that length biases in the errors of these correctness
functions distort UQ assessments by interacting with length biases in UQ
methods. We identify LLM-as-a-judge approaches as among the least length-biased
choices and hence a potential solution to mitigate these biases.
6806a3464020c8351ddd6b90
Andrea Santilli
claimed_verified
2025-04-22T09:51:54.306Z
5e8ef1f14957053f606489e6
https://cdn-avatars.huggingface.co/v1/production/uploads/1635502086699-5e8ef1f14957053f606489e6.jpeg
Andrea Santilli
user
teelinsan
6806a3464020c8351ddd6b91
Adam Golinski
6806a3464020c8351ddd6b92
Michael Kirchhof
6806a3464020c8351ddd6b93
Federico Danieli
6806a3464020c8351ddd6b94
Arno Blaas
6806a3464020c8351ddd6b95
Miao Xiong
claimed_verified
2025-05-27T10:03:45.808Z
6530cf34e7535baecd9620a7
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Miao Xiong
user
happymio
6806a3464020c8351ddd6b96
Luca Zappella
6806a3464020c8351ddd6b97
Sinead Williamson
Revisiting Uncertainty Quantification Evaluation in Language Models:
  Spurious Interactions with Response Length Bias Results
Uncertainty Quantification (UQ) in Language Models (LMs) is crucial for
improving their safety and reliability. Evaluations often use performance
metrics like AUROC to assess how well UQ methods (e.g., negative sequence
probabilities) correlate with task correctness functions (e.g., ROUGE-L). In
this paper, we show that commonly used correctness functions bias UQ
evaluations by inflating the performance of certain UQ methods. We evaluate 7
correctness functions -- from lexical-based and embedding-based metrics to
LLM-as-a-judge approaches -- across 4 datasets x 4 models x 6 UQ methods. Our
analysis reveals that length biases in the errors of these correctness
functions distort UQ assessments by interacting with length biases in UQ
methods. We identify LLM-as-a-judge approaches as among the least length-biased
choices and hence a potential solution to mitigate these biases.
Evaluations of Uncertainty Quantification (UQ) in language models are biased by correctness functions, affecting UQ methods' performance, with LLM-as-a-judge approaches identified as less biased.
Uncertainty Quantification (UQ) Language Models (LMs) AUROC negative sequence probabilities ROUGE-L lexical-based metrics embedding-based metrics length biases LLM-as-a-judge approaches