A Conformal Risk Control Framework for Granular Word Assessment and Uncertainty Calibration of CLIPScore Quality Estimates

This study explores current limitations of learned image captioning evaluation metrics, specifically the lack of granular assessment for individual word misalignments within captions, and the reliance on single-point quality estimates without considering uncertainty. To address these limitations, we propose a simple yet effective strategy for generating and calibrating CLIPScore distributions. Leveraging a model-agnostic conformal risk control framework, we calibrate CLIPScore values for task-specific control variables, to tackle the aforementioned two limitations. Experimental results demonstrate that using conformal risk control, over the distributions produced with simple methods such as input masking, can achieve competitive performance compared to more complex approaches. Our method effectively detects misaligned words, while providing formal guarantees aligned with desired risk levels, and improving the correlation between uncertainty estimations and prediction errors, thus enhancing the overall reliability of caption evaluation metrics.