Search Results for author: Rachit Bansal

Found 10 papers, 6 papers with code

Measures of Information Reflect Memorization Patterns

no code implementations17 Oct 2022 Rachit Bansal, Danish Pruthi, Yonatan Belinkov

In this work, we hypothesize -- and subsequently show -- that the diversity in the activation patterns of different neurons is reflective of model generalization and memorization.

Memorization Model Selection

LM-CORE: Language Models with Contextually Relevant External Knowledge

1 code implementation Findings (NAACL) 2022 Jivat Neet Kaur, Sumit Bhatia, Milan Aggarwal, Rachit Bansal, Balaji Krishnamurthy

Large transformer-based pre-trained language models have achieved impressive performance on a variety of knowledge-intensive tasks and can capture factual knowledge in their parameters.

Knowledge Probing Language Modelling

Linear Connectivity Reveals Generalization Strategies

1 code implementation24 May 2022 Jeevesh Juneja, Rachit Bansal, Kyunghyun Cho, João Sedoc, Naomi Saphra

It is widely accepted in the mode connectivity literature that when two neural networks are trained similarly on the same data, they are connected by a path through parameter space over which test set accuracy is maintained.


No Need to Know Everything! Efficiently Augmenting Language Models With External Knowledge

no code implementations AKBC Workshop CSKB 2021 Jivat Neet Kaur, Sumit Bhatia, Milan Aggarwal, Rachit Bansal, Balaji Krishnamurthy

This allows the training of the language model to be de-coupled from the external knowledge source and the latter can be updated without affecting the parameters of the language model.

Language Modelling

How Low is Too Low? A Computational Perspective on Extremely Low-Resource Languages

2 code implementations ACL 2021 Rachit Bansal, Himanshu Choudhary, Ravneet Punia, Niko Schenk, Jacob L Dahl, Émilie Pagé-Perron

Despite the recent advancements of attention-based deep learning architectures across a majority of Natural Language Processing tasks, their application remains limited in a low-resource setting because of a lack of pre-trained models for such languages.

Machine Translation named-entity-recognition +4

Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets

1 code implementation12 Apr 2021 Rachit Bansal, William Scott Paka, Nidhi, Shubhashis Sengupta, Tanmoy Chakraborty

In this work, we present ENDEMIC, a novel early detection model which leverages exogenous and endogenous signals related to tweets, while learning on limited labeled data.

Graph Embedding Misinformation

Evaluating Explanations: How much do explanations from the teacher aid students?

1 code implementation1 Dec 2020 Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig, William W. Cohen

While many methods purport to explain predictions by highlighting salient features, what aims these explanations serve and how they ought to be evaluated often go unstated.

Question Answering text-classification +1

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