3 citations · 13 across the 14 of their papers we have counts for
15 papers
Hopfield Networks in Non-volatile Memory
Patrick Miller, Ulya R. Karpuzcu
Many important computational workloads are bound by the performance of the memory system that supplies data to conventional processors. Compute in Memory (CiM) promises to circumve…
MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding
Sai Munikoti, Ian Stewart, Chengping Chai +4
The application of generalist multimodal models (GMMs) to specialized scientific domains remains limited due to the scarcity of comprehensive domain-specific datasets that integrat…
Back to the Barn with LLAMAs: Evolving Pretrained LLM Backbones in Finetuning Vision Language Models
Sameera Horawalavithana, Lauren Phillips, Ian Stewart +2
Vision-Language Models (VLMs) have rapidly advanced by leveraging powerful pre-trained Large Language Models (LLMs) as core reasoning backbones. As new and more capable LLMs emerge…
Directional Concentration Uncertainty: A representational approach to uncertainty quantification for generative models
Souradeep Chattopadhyay, Brendan Kennedy, Sai Munikoti +2
In the critical task of making generative models trustworthy and robust, methods for Uncertainty Quantification (UQ) have begun to show encouraging potential. However, many of thes…
Audit, Alignment, and Optimization of LM-Powered Subroutines with Application to Public Comment Processing
Reilly Raab, Mike Parker, Dan Nally +4
The advent of language models (LMs) has the potential to dramatically accelerate tasks that may be cast to text-processing; however, real-world adoption is hindered by concerns reg…
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…
Surprisingly Fragile: Assessing and Addressing Prompt Instability in Multimodal Foundation Models
Ian Stewart, Sameera Horawalavithana, Brendan Kennedy +2
Multimodal foundation models (MFMs) such as OFASys show the potential to unlock analysis of complex data such as images, videos, and audio data via text prompts alone. However, the…
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…
Generalist Multimodal AI: A Review of Architectures, Challenges and Opportunities
Sai Munikoti, Ian Stewart, Sameera Horawalavithana +4
Multimodal models are expected to be a critical component to future advances in artificial intelligence. This field is starting to grow rapidly with a surge of new design elements…
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,…
SCITUNE: Aligning Large Language Models with Human-Curated Scientific Multimodal Instructions
Sameera Horawalavithana, Sai Munikoti, Ian Stewart +2
Instruction finetuning is a popular paradigm to align large language models (LLM) with human intent. Despite its popularity, this idea is less explored in improving LLMs to align e…