3 citations · 4 across the 8 of their papers we have counts for
8 papers
WeQA: A Benchmark for Retrieval Augmented Generation in Wind Energy Domain
Rounak Meyur, Hung Phan, Sridevi Wagle +5
Wind energy project assessments present significant challenges for decision-makers, who must navigate and synthesize hundreds of pages of environmental and scientific documentation…
ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science
Sai Munikoti, Anurag Acharya, Sridevi Wagle +1
Large language models record impressive performance on many natural language processing tasks. However, their knowledge capacity is limited to the pretraining corpus. Retrieval aug…
Empirical evaluation of Uncertainty Quantification in Retrieval-Augmented Language Models for Science
Sridevi Wagle, Sai Munikoti, Anurag Acharya +2
Large language models (LLMs) have shown remarkable achievements in natural language processing tasks, producing high-quality outputs. However, LLMs still exhibit limitations, inclu…
Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning
Sai Munikoti, Anurag Acharya, Sridevi Wagle +1
Despite the dramatic progress in Large Language Model (LLM) development, LLMs often provide seemingly plausible but not factual information, often referred to as hallucinations. Re…
NuclearQA: A Human-Made Benchmark for Language Models for the Nuclear Domain
Anurag Acharya, Sai Munikoti, Aaron Hellinger +3
As LLMs have become increasingly popular, they have been used in almost every field. But as the application for LLMs expands from generic fields to narrow, focused science domains,…
Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling
Boris Kovalerchuk Andrew Dunn, Alex Worland, Sridevi Wagle
To increase the interpretability and prediction accuracy of the Machine Learning (ML) models, visualization of ML models is a key part of the ML process. Decision Trees (DTs) are e…
Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models
Alex Worland, Sridevi Wagle, Boris Kovalerchuk
Visualization of Machine Learning (ML) models is an important part of the ML process to enhance the interpretability and prediction accuracy of the ML models. This paper proposes a…
Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning
Sridevi Narayana Wagle, Boris Kovalerchuk
Machine learning algorithms often produce models considered as complex black-box models by both end users and developers. They fail to explain the model in terms of the domain they…