CLIPScore uses a web-pretrained CLIP model to evaluate image captions without references and achieves higher human correlation than CIDEr or SPICE.
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Deduplicating training datasets reduces language model verbatim memorization by 10x, improves training efficiency, and enables more accurate evaluation by cutting train-test overlap.
Fixed-width and decay-based attention mechanisms inspired by working memory improve Transformer grammatical accuracy and human alignment under limited training data.
citing papers explorer
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CLIPScore: A Reference-free Evaluation Metric for Image Captioning
CLIPScore uses a web-pretrained CLIP model to evaluate image captions without references and achieves higher human correlation than CIDEr or SPICE.
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Deduplicating Training Data Makes Language Models Better
Deduplicating training datasets reduces language model verbatim memorization by 10x, improves training efficiency, and enables more accurate evaluation by cutting train-test overlap.
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Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity
Fixed-width and decay-based attention mechanisms inspired by working memory improve Transformer grammatical accuracy and human alignment under limited training data.