Revisiting Uncertainty Quantification Evaluation in Language Models:
  Spurious Interactions with Response Length Bias Results
Authors: name: Andrea Santilli, name: Adam Golinski, name: Michael Kirchhof, name: Federico Danieli, name: Arno Blaas, name: Miao Xiong, name: Luca Zappella, name: Sinead Williamson

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.