122 citations · 123 across the 6 of their papers we have counts for
6 papers
MetaOthello: A Controlled Study of Multiple World Models in Transformers
Aviral Chawla, Galen Hall, Juniper Lovato
Foundation models must handle multiple generative processes, yet mechanistic interpretability largely studies capabilities in isolation; it remains unclear how a single transformer…
Multi Document Reading Comprehension
Avi Chawla
Reading Comprehension (RC) is a task of answering a question from a given passage or a set of passages. In the case of multiple passages, the task is to find the best possible answ…
KARL-Trans-NER: Knowledge Aware Representation Learning for Named Entity Recognition using Transformers
Avi Chawla, Nidhi Mulay, Vikas Bishnoi +1
The inception of modeling contextual information using models such as BERT, ELMo, and Flair has significantly improved representation learning for words. It has also given SOTA res…
A Comparative Study of Transformers on Word Sense Disambiguation
Avi Chawla, Nidhi Mulay, Vikas Bishnoi +2
Recent years of research in Natural Language Processing (NLP) have witnessed dramatic growth in training large models for generating context-aware language representations. In this…
Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings
Gregor Wiedemann, Steffen Remus, Avi Chawla +1
Contextualized word embeddings (CWE) such as provided by ELMo (Peters et al., 2018), Flair NLP (Akbik et al., 2018), or BERT (Devlin et al., 2019) are a major recent innovation in…
IIT (BHU) Varanasi at MSR-SRST 2018: A Language Model Based Approach for Natural Language Generation
Shreyansh Singh, Avi Chawla, Ayush Sharma +1
This paper describes our submission system for the Shallow Track of Surface Realization Shared Task 2018 (SRST'18). The task was to convert genuine UD structures, from which word o…