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.

Evaluation of Multilingual Image Captioning: How far can we get with CLIP models?

The evaluation of image captions, looking at both linguistic fluency and semantic correspondence to visual contents, has witnessed a significant effort. Still, despite advancements such as the CLIPScore metric, multilingual captioning evaluation has remained relatively unexplored. This work presents several strategies, and extensive experiments, related to evaluating CLIPScore variants in multilingual settings. To address the lack of multilingual test data, we consider two different strategies: (1) using quality aware machine-translated datasets with human judgements, and (2) re-purposing multilingual datasets that target semantic inference and reasoning. Our results highlight the potential of finetuned multilingual models to generalize across languages and to handle complex linguistic challenges. Tests with machine-translated data show that multilingual CLIPScore models can maintain a high correlation with human judgements across different languages, and additional tests with natively multilingual and multicultural data further attest to the high-quality assessments.

Accurate and Well-Calibrated ICD Code Assignment Through Attention Over Diverse Label Embeddings

Although the International Classification of Diseases (ICD) has been adopted worldwide, manually assigning ICD codes to clinical text is time-consuming, error-prone, and expensive, motivating the development of automated approaches. This paper describes a novel approach for automated ICD coding, combining several ideas from previous related work. We specifically employ a strong Transformer-based model as a text encoder and, to handle lengthy clinical narratives, we explored either (a) adapting the base encoder model into a Longformer, or (b) dividing the text into chunks and processing each chunk independently. The representations produced by the encoder are combined with a label embedding mechanism that explores diverse ICD code synonyms. Experiments with different splits of the MIMIC-III dataset show that the proposed approach outperforms the current state-of-the-art models in ICD coding, with the label embeddings significantly contributing to the good performance. Our approach also leads to properly calibrated classification results, which can effectively inform downstream tasks such as quantification.