SMURF: SeMantic and linguistic UndeRstanding Fusion for Caption Evaluation via Typicality Analysis (2021.acl-long)
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| Challenge: | Visual captioning is an open-ended area for evaluation, requiring specialized training to improve human-correlation. |
| Approach: | They propose a new evaluation framework rooted in information theory . they propose metric SPURTS and metric SMURF to measure fluency . |
| Outcome: | The proposed metrics achieve state-of-the-art correlation with human judgment compared with other evaluation metrics. |
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| Challenge: | Existing image captioning metrics focus on linguistic aspects and do not match human judgements at sentence-level. |
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Transparent Human Evaluation for Image Captioning (2022.naacl-main)
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Jungo Kasai, Keisuke Sakaguchi, Lavinia Dunagan, Jacob Morrison, Ronan Le Bras, Yejin Choi, Noah A. Smith
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| Challenge: | elucidates the dangerous current state of style transfer auto-evaluation research. |
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| Challenge: | Existing measures for image caption evaluation fail to capture dimensions of similarity . a novel method that leverages the zero-shot language modeling capabilities of large language models (LLMs) demonstrates a stronger correlation with human judgments of caption quality compared to existing measures. |
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SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models (2025.naacl-industry)
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| Challenge: | Large language models (LLMs) have emerged as key drivers of progress in the field of natural language processing. |
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