10 citations · 21 across the 9 of their papers we have counts for
9 papers
LLM Augmented LLMs: Expanding Capabilities through Composition
Rachit Bansal, Bidisha Samanta, Siddharth Dalmia +6
Foundational models with billions of parameters which have been trained on large corpora of data have demonstrated non-trivial skills in a variety of domains. However, due to their…
Measures of Information Reflect Memorization Patterns
Rachit Bansal, Danish Pruthi, Yonatan Belinkov
Neural networks are known to exploit spurious artifacts (or shortcuts) that co-occur with a target label, exhibiting heuristic memorization. On the other hand, networks have been s…
LM-CORE: Language Models with Contextually Relevant External Knowledge
Jivat Neet Kaur, Sumit Bhatia, Milan Aggarwal +2
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 parame…
CoSe-Co: Text Conditioned Generative CommonSense Contextualizer
Rachit Bansal, Milan Aggarwal, Sumit Bhatia +2
Pre-trained Language Models (PTLMs) have been shown to perform well on natural language tasks. Many prior works have leveraged structured commonsense present in the form of entitie…
Linear Connectivity Reveals Generalization Strategies
Jeevesh Juneja, Rachit Bansal, Kyunghyun Cho +2
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…
How Low is Too Low? A Computational Perspective on Extremely Low-Resource Languages
Rachit Bansal, Himanshu Choudhary, Ravneet Punia +3
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-re…
Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets
Rachit Bansal, William Scott Paka, Nidhi +2
Fake tweets are observed to be ever-increasing, demanding immediate countermeasures to combat their spread. During COVID-19, tweets with misinformation should be flagged and neutra…
Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection
William Scott Paka, Rachit Bansal, Abhay Kaushik +2
As the COVID-19 pandemic sweeps across the world, it has been accompanied by a tsunami of fake news and misinformation on social media. At the time when reliable information is vit…
Evaluating Explanations: How much do explanations from the teacher aid students?
Danish Pruthi, Rachit Bansal, Bhuwan Dhingra +5
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. In this…