37 citations · 52 across the 13 of their papers we have counts for
13 papers
FLARE: Few-shot Learning-based Adaptive Reflective Engine
Dhanasekar Sundararaman, Bharat Gandhi, Aashna Garg +1
Large language models (LLMs) are increasingly deployed in complex, compound AI systems where performance hinges on the quality of prompts. Recent state-of-the-art optimizers like G…
LOCUS: A System and Method for Low-Cost Customization for Universal Specialization
Dhanasekar Sundararaman, Keying Li, Wayne Xiong +1
We present LOCUS (LOw-cost Customization for Universal Specialization), a pipeline that consumes few-shot data to streamline the construction and training of NLP models through tar…
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…
Improving Downstream Task Performance by Treating Numbers as Entities
Dhanasekar Sundararaman, Vivek Subramanian, Guoyin Wang +2
Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed. Though numbers are typically not a…
How do lexical semantics affect translation? An empirical study
Vivek Subramanian, Dhanasekar Sundararaman
Neural machine translation (NMT) systems aim to map text from one language into another. While there are a wide variety of applications of NMT, one of the most important is transla…
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…
How much is my car worth? A methodology for predicting used cars prices using Random Forest
Nabarun Pal, Priya Arora, Dhanasekar Sundararaman +2
Cars are being sold more than ever. Developing countries adopt the lease culture instead of buying a new car due to affordability. Therefore, the rise of used cars sales is exponen…
Rating the online review rating system using Yelp
Dhanasekar S, Balaji
The impact of ratings on a restaurant plays a major role in attracting future customers to that restaurant. The word of mouth has been systematically replaced with the online revie…
Twigraph: Discovering and Visualizing Influential Words between Twitter Profiles
Dhanasekar Sundararaman, Sudharshan Srinivasan
The social media craze is on an ever increasing spree, and people are connected with each other like never before, but these vast connections are visually unexplored. We propose a…