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Francesca Franzon

3 accepted papers

2023

Cross-Domain Image Captioning With Discriminative Finetuning

CVPR 2023poster

Neural captioners are typically trained to mimic human-generated references without optimizing for any specific communication goal, leading to problems such as the generation of vague captions. In this paper, we show that fine-tuning an out-of-the-box neural captioner with a self-supervised discrimi…

2023

Unnatural language processing: How do language models handle machine-generated prompts?

EMNLP 2023long findings

Language model prompt optimization research has shown that semantically and grammatically well-formed manually crafted prompts are routinely outperformed by automatically generated token sequences with no apparent meaning or syntactic structure, including sequences of vectors from a model's embeddin…

Cited by 0SourceScholar
2022

Communication breakdown: On the low mutual intelligibility between human and neural captioning

EMNLP 2022main

We compare the 0-shot performance of a neural caption-based image retriever when given as input either human-produced captions or captions generated by a neural captioner. We conduct this comparison on the recently introduced ImageCoDe data-set (Krojer et al. 2022), which contains hard distractors n…