2 citations · 5 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…