Search Results for author:
Found 11 papers, 7 papers with code
LLM Augmented LLMs: Expanding Capabilities through Composition
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.
Measures of Information Reflect Memorization Patterns
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.
LM-CORE: Language Models with Contextually Relevant External Knowledge
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.
CoSe-Co: Text Conditioned Generative CommonSense Contextualizer
To train CoSe-Co, we propose a novel dataset comprising of sentence and commonsense knowledge pairs.
Linear Connectivity Reveals Generalization Strategies
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.
CoSe-Co: Sentence Conditioned Generative CommonSense Contextualizer for Language Models
Pre-trained Language Models (PTLMs) have been shown to perform well on natural language reasoning tasks requiring commonsense.
No Need to Know Everything! Efficiently Augmenting Language Models With External Knowledge
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.
How Low is Too Low? A Computational Perspective on Extremely Low-Resource Languages
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.
Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets
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.
Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection
As the COVID-19 pandemic sweeps across the world, it has been accompanied by a tsunami of fake news and misinformation on social media.
Evaluating Explanations: How much do explanations from the teacher aid students?
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.
