3 citations · 6 across the 11 of their papers we have counts for
11 papers
Evaluating the Robustness of Dense Retrievers in Interdisciplinary Domains
Sarthak Chaturvedi, Anurag Acharya, Rounak Meyur +3
Evaluation benchmark characteristics may distort the true benefits of domain adaptation in retrieval models. This creates misleading assessments that influence deployment decisions…
DISHONEST: Dissecting misInformation Spread using Homogeneous sOcial NEtworks and Semantic Topic classification
Caleb Stam, Emily Saldanha, Mahantesh Halappanavar +1
The emergence of the COVID-19 pandemic resulted in a significant rise in the spread of misinformation on online platforms such as Twitter. Oftentimes this growth is blamed on the i…
Exploring the Benefits of Domain-Pretraining of Generative Large Language Models for Chemistry
Anurag Acharya, Shivam Sharma, Robin Cosbey +3
A proliferation of Large Language Models (the GPT series, BLOOM, LLaMA, and more) are driving forward novel development of multipurpose AI for a variety of tasks, particularly natu…
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…
Benchmarking LLMs for Environmental Review and Permitting
Rounak Meyur, Hung Phan, Koby Hayashi +12
The National Environment Policy Act (NEPA) stands as a foundational piece of environmental legislation in the United States, requiring federal agencies to consider the environmenta…
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,…
Finding Trolls Under Bridges: Preliminary Work on a Motif Detector
W. Victor H. Yarlott, Armando Ochoa, Anurag Acharya +7
Motifs are distinctive recurring elements found in folklore that have significance as communicative devices in news, literature, press releases, and propaganda. Motifs concisely im…
Towards an Atlas of Cultural Commonsense for Machine Reasoning
Anurag Acharya, Kartik Talamadupula, Mark A Finlayson
Existing commonsense reasoning datasets for AI and NLP tasks fail to address an important aspect of human life: cultural differences. We introduce an approach that extends prior wo…