October 27, 2022
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.
Written by
Roberto Dessì
Eleonora Gualdoni
Francesca Franzon
Gemma Boleda
Marco Baroni
Publisher
EMNLP
Research Topics
December 12, 2024
Melanie Sclar, Jane Yu, Maryam Fazel-Zarandi, Yulia Tsvetkov, Yonatan Bisk, Yejin Choi, Asli Celikyilmaz
December 12, 2024
October 31, 2024
Mike Lambeta, Tingfan Wu, Ali Sengül, Victoria Rose Most, Nolan Black, Kevin Sawyer, Romeo Mercado, Haozhi Qi, Alexander Sohn, Byron Taylor, Norb Tydingco, Gregg Kammerer, Dave Stroud, Jake Khatha, Kurt Jenkins, Kyle Most, Neal Stein, Ricardo Chavira, Thomas Craven-Bartle, Eric Sanchez, Yitian Ding, Jitendra Malik, Roberto Calandra
October 31, 2024
October 04, 2024
Bandhav Veluri, Benjamin Peloquin, Bokai Yu, Hongyu Gong, Shyam Gollakota
October 04, 2024
July 23, 2024
Llama team
July 23, 2024
Foundational models
Our approach
Latest news
Foundational models