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Found 5 papers, 3 papers with code
Unnatural language processing: How do language models handle machine-generated prompts?
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 embedding space.
Cross-Domain Image Captioning with Discriminative Finetuning
However, when the model is used without further tuning to generate captions for out-of-domain datasets, our discriminatively-finetuned captioner generates descriptions that resemble human references more than those produced by the same captioner without finetuning.
Referential communication in heterogeneous communities of pre-trained visual deep networks
As a first step in this direction, we systematically explore the task of \textit{referential communication} in a community of heterogeneous state-of-the-art pre-trained visual networks, showing that they can develop, in a self-supervised way, a shared protocol to refer to a target object among a set of candidates.
Communication breakdown: On the low mutual intelligibility between human and neural captioning
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.
