no code implementations • Findings (EMNLP) 2021 • Aashi Jain, Mandy Guo, Krishna Srinivasan, Ting Chen, Sneha Kudugunta, Chao Jia, Yinfei Yang, Jason Baldridge
Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages.
no code implementations • 14 Sep 2023 • Lingyu Gao, Aditi Chaudhary, Krishna Srinivasan, Kazuma Hashimoto, Karthik Raman, Michael Bendersky
In-context learning (ICL) i. e. showing LLMs only a few task-specific demonstrations has led to downstream gains with no task-specific fine-tuning required.
no code implementations • 19 May 2023 • Aditi Chaudhary, Karthik Raman, Krishna Srinivasan, Kazuma Hashimoto, Mike Bendersky, Marc Najork
While our experiments demonstrate that these modifications help improve performance of QGen techniques, we also find that QGen approaches struggle to capture the full nuance of the relevance label space and as a result the generated queries are not faithful to the desired relevance label.
2 code implementations • 9 May 2023 • Andrea Burns, Krishna Srinivasan, Joshua Ainslie, Geoff Brown, Bryan A. Plummer, Kate Saenko, Jianmo Ni, Mandy Guo
Webpages have been a rich resource for language and vision-language tasks.
1 code implementation • 5 May 2023 • Andrea Burns, Krishna Srinivasan, Joshua Ainslie, Geoff Brown, Bryan A. Plummer, Kate Saenko, Jianmo Ni, Mandy Guo
Webpages have been a rich, scalable resource for vision-language and language only tasks.
2 code implementations • 4 Apr 2023 • Jheng-Hong Yang, Carlos Lassance, Rafael Sampaio de Rezende, Krishna Srinivasan, Miriam Redi, Stéphane Clinchant, Jimmy Lin
This paper presents the AToMiC (Authoring Tools for Multimedia Content) dataset, designed to advance research in image/text cross-modal retrieval.
no code implementations • 27 Oct 2022 • Krishna Srinivasan, Karthik Raman, Anupam Samanta, Lingrui Liao, Luca Bertelli, Mike Bendersky
Thus, in this paper we make the following contributions: (1) We demonstrate that Retrieval Augmentation of queries provides LLMs with valuable additional context enabling improved understanding.
no code implementations • 16 Mar 2022 • Karthik Raman, Iftekhar Naim, Jiecao Chen, Kazuma Hashimoto, Kiran Yalasangi, Krishna Srinivasan
Pretrained, large, generative language models (LMs) have had great success in a wide range of sequence tagging and structured prediction tasks.
no code implementations • 10 Sep 2021 • Aashi Jain, Mandy Guo, Krishna Srinivasan, Ting Chen, Sneha Kudugunta, Chao Jia, Yinfei Yang, Jason Baldridge
Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages.
Ranked #1 on Semantic Image-Text Similarity on CxC
3 code implementations • 2 Mar 2021 • Krishna Srinivasan, Karthik Raman, Jiecao Chen, Michael Bendersky, Marc Najork
First, WIT is the largest multimodal dataset by the number of image-text examples by 3x (at the time of writing).
Ranked #1 on Image Retrieval on WIT
no code implementations • 23 Oct 2020 • Aditi Chaudhary, Karthik Raman, Krishna Srinivasan, Jiecao Chen
In particular, by requiring the model to predict the language-specific token, the MLM objective disincentivizes learning a language-agnostic representation -- which is a key goal of multilingual pre-training.