7 citations · 18 across the 13 of their papers we have counts for
14 papers
TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG
Savini Kashmira, Jayanaka L. Dantanarayana, Joshua Brodsky +5
Retrieval-Augmented Generation (RAG) is one of the leading and most widely used techniques for enhancing LLM retrieval capabilities, but it still faces significant limitations in c…
PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization
Christopher Clarke, Yuzhao Heng, Lingjia Tang +1
The recent emergence of Large Language Models (LLMs) has heralded a new era of human-AI interaction. These sophisticated models, exemplified by Chat-GPT and its successors, have ex…
MTP: A Meaning-Typed Language Abstraction for AI-Integrated Programming
Jayanaka L. Dantanarayana, Yiping Kang, Kugesan Sivasothynathan +6
Software development is shifting from traditional programming to AI-integrated applications that leverage generative AI and large language models (LLMs) during runtime. However, in…
One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI
Christopher Clarke, Karthik Krishnamurthy, Walter Talamonti +3
Conversational agents have been gaining increasing popularity in recent years. Influenced by the widespread adoption of task-oriented agents such as Apple Siri and Amazon Alexa, th…
Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production
Chandra Irugalbandara, Ashish Mahendra, Roland Daynauth +6
Many companies use large language models (LLMs) offered as a service, like OpenAI's GPT-4, to create AI-enabled product experiences. Along with the benefits of ease-of-use and shor…
Label Agnostic Pre-training for Zero-shot Text Classification
Christopher Clarke, Yuzhao Heng, Yiping Kang +3
Conventional approaches to text classification typically assume the existence of a fixed set of predefined labels to which a given text can be classified. However, in real-world ap…
The Jaseci Programming Paradigm and Runtime Stack: Building Scale-out Production Applications Easy and Fast
Jason Mars, Yiping Kang, Roland Daynauth +4
Today's production scale-out applications include many sub-application components, such as storage backends, logging infrastructure and AI models. These components have drastically…
The Case for a Wholistic Serverless Programming Paradigm and Full Stack Automation for AI and Beyond -- The Philosophy of Jaseci and Jac
Jason Mars
In this work, the case is made for a wholistic top-down re-envisioning of the system stack from the programming language level down through the system architecture to bridge this c…
One Agent To Rule Them All: Towards Multi-agent Conversational AI
Christopher Clarke, Joseph Joshua Peper, Karthik Krishnamurthy +6
The increasing volume of commercially available conversational agents (CAs) on the market has resulted in users being burdened with learning and adopting multiple agents to accompl…
Towards Personalized Intelligence at Scale
Yiping Kang, Ashish Mahendra, Christopher Clarke +2
Personalized Intelligence (PI) is the problem of providing customized AI experiences tailored to each individual user. In many applications, PI is preferred or even required. Exist…
A Benchmarking Framework for Interactive 3D Applications in the Cloud
Tianyi Liu, Sen He, Sunzhou Huang +4
With the growing popularity of cloud gaming and cloud virtual reality (VR), interactive 3D applications have become a major type of workloads for the cloud. However, despite their…
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
Stefan Larson, Anish Mahendran, Joseph J. Peper +8
Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover eve…
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems
Stefan Larson, Anish Mahendran, Andrew Lee +6
In a corpus of data, outliers are either errors: mistakes in the data that are counterproductive, or are unique: informative samples that improve model robustness. Identifying outl…
Rethinking Numerical Representations for Deep Neural Networks
Parker Hill, Babak Zamirai, Shengshuo Lu +9
With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN model weights and act…