37 citations · 52 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…