5 citations · 5 across the 6 of their papers we have counts for
8 papers
What do Large Language Models Learn beyond Language?
Avinash Madasu, Shashank Srivastava
Large language models (LMs) have rapidly become a mainstay in Natural Language Processing. These models are known to acquire rich linguistic knowledge from training on large amount…
CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations
Rakesh R Menon, Sayan Ghosh, Shashank Srivastava
Supervised learning has traditionally focused on inductive learning by observing labeled examples of a task. In contrast, humans have the ability to learn new concepts from languag…
Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement Learning
Sayan Ghosh, Shashank Srivastava
Mapping natural language instructions to programs that computers can process is a fundamental challenge. Existing approaches focus on likelihood-based training or using reinforceme…
Adversarial Scrubbing of Demographic Information for Text Classification
Somnath Basu Roy Chowdhury, Sayan Ghosh, Yiyuan Li +3
Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated targ…
Improving and Simplifying Pattern Exploiting Training
Derek Tam, Rakesh R Menon, Mohit Bansal +2
Recently, pre-trained language models (LMs) have achieved strong performance when fine-tuned on difficult benchmarks like SuperGLUE. However, performance can suffer when there are…
PRover: Proof Generation for Interpretable Reasoning over Rules
Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava +1
Recent work by Clark et al. (2020) shows that transformers can act as 'soft theorem provers' by answering questions over explicitly provided knowledge in natural language. In our w…