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

Abstract

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 nearly identical to the images to be retrieved. We find that the neural retriever has much higher performance when fed neural rather than human captions, despite the fact that the former, unlike the latter, were generated without awareness of the distractors that make the task hard. Even more remarkably, when the same neural captions are given to human subjects, their retrieval performance is almost at chance level. Our results thus add to the growing body of evidence that, even when the "language" of neural models resembles English, this superficial resemblance might be deeply misleading.

Toward More Human-Like AI Communication: A Review of Emergent Communication Research

“…Human-centered EmCom research is developed in game settings and thus employs reinforcement learning, whereas IC predominantly uses supervised learning. While Hum-EmCom explicitly models the interaction among multiple agents with a shared goal, IC is focused on architectures capable of mimicking the human ability to use language in a visual context, which may not align with human understanding [168]. As both fields aim to refine artificial languages to better resemble human-like ones, they should be regarded as complementary components of the broader challenge of achieving this goal.…”