Search Results for author: Gargi Ghosh

Found 14 papers, 8 papers with code

Demystifying CLIP Data

2 code implementations28 Sep 2023 Hu Xu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer, Christoph Feichtenhofer

We believe that the main ingredient to the success of CLIP is its data and not the model architecture or pre-training objective.

LIMA: Less Is More for Alignment

5 code implementations NeurIPS 2023 Chunting Zhou, PengFei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, Omer Levy

Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences.

Language Modelling reinforcement-learning

CiT: Curation in Training for Effective Vision-Language Data

1 code implementation ICCV 2023 Hu Xu, Saining Xie, Po-Yao Huang, Licheng Yu, Russell Howes, Gargi Ghosh, Luke Zettlemoyer, Christoph Feichtenhofer

Large vision-language models are generally applicable to many downstream tasks, but come at an exorbitant training cost that only large institutions can afford.

ALERT: Adapting Language Models to Reasoning Tasks

no code implementations16 Dec 2022 Ping Yu, Tianlu Wang, Olga Golovneva, Badr Alkhamissy, Gargi Ghosh, Mona Diab, Asli Celikyilmaz

Current large language models can perform reasonably well on complex tasks that require step-by-step reasoning with few-shot learning.

Few-Shot Learning Language Modelling +1

CM3: A Causal Masked Multimodal Model of the Internet

no code implementations19 Jan 2022 Armen Aghajanyan, Bernie Huang, Candace Ross, Vladimir Karpukhin, Hu Xu, Naman Goyal, Dmytro Okhonko, Mandar Joshi, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer

We introduce CM3, a family of causally masked generative models trained over a large corpus of structured multi-modal documents that can contain both text and image tokens.

Entity Disambiguation Entity Linking

VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding

2 code implementations EMNLP 2021 Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, Christoph Feichtenhofer

We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot video and text understanding, without using any labels on downstream tasks.

 Ranked #1 on Temporal Action Localization on CrossTask (using extra training data)

Action Segmentation Long Video Retrieval (Background Removed) +4

Pre-training via Paraphrasing

2 code implementations NeurIPS 2020 Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, Luke Zettlemoyer

The objective noisily captures aspects of paraphrase, translation, multi-document summarization, and information retrieval, allowing for strong zero-shot performance on several tasks.

Document Summarization Document Translation +6

Optimizing Query Evaluations using Reinforcement Learning for Web Search

no code implementations12 Apr 2018 Corby Rosset, Damien Jose, Gargi Ghosh, Bhaskar Mitra, Saurabh Tiwary

In web search, typically a candidate generation step selects a small set of documents---from collections containing as many as billions of web pages---that are subsequently ranked and pruned before being presented to the user.

reinforcement-learning Reinforcement Learning (RL)

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