37 citations · 48 across the 8 of their papers we have counts for
8 papers
Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities
Shounak Datta, Dhanasekar Sundararaman
Despite their impressive performance on multi-modal tasks, large vision-language models (LVLMs) tend to suffer from hallucinations. An important type is object hallucination, where…
Pseudo-OOD training for robust language models
Dhanasekar Sundararaman, Nikhil Mehta, Lawrence Carin
While pre-trained large-scale deep models have garnered attention as an important topic for many downstream natural language processing (NLP) tasks, such models often make unreliab…
Debiasing Gender Bias in Information Retrieval Models
Dhanasekar Sundararaman, Vivek Subramanian
Biases in culture, gender, ethnicity, etc. have existed for decades and have affected many areas of human social interaction. These biases have been shown to impact machine learnin…
Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding
Dhanasekar Sundararaman, Vivek Subramanian, Guoyin Wang +4
Attention-based models have shown significant improvement over traditional algorithms in several NLP tasks. The Transformer, for instance, is an illustrative example that generates…
Learning Compressed Sentence Representations for On-Device Text Processing
Dinghan Shen, Pengyu Cheng, Dhanasekar Sundararaman +5
Vector representations of sentences, trained on massive text corpora, are widely used as generic sentence embeddings across a variety of NLP problems. The learned representations a…
TweetIT- Analyzing Topics for Twitter Users to garner Maximum Attention
Dhanasekar Sundararaman, Priya Arora, Vishwanath Seshagiri
Twitter, a microblogging service, is todays most popular platform for communication in the form of short text messages, called Tweets. Users use Twitter to publish their content ei…