1 citations · 1 across the 4 of their papers we have counts for
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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…
Aligning Model Evaluations with Human Preferences: Mitigating Token Count Bias in Language Model Assessments
Roland Daynauth, Jason Mars
The SLAM paper demonstrated that on-device Small Language Models (SLMs) are a viable and cost-effective alternative to API-based Large Language Models (LLMs), such as OpenAI's GPT-…
Guylingo: The Republic of Guyana Creole Corpora
Christopher Clarke, Roland Daynauth, Charlene Wilkinson +2
While major languages often enjoy substantial attention and resources, the linguistic diversity across the globe encompasses a multitude of smaller, indigenous, and regional langua…
Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection
Christopher Clarke, Matthew Hall, Gaurav Mittal +4
Classic approaches to content moderation typically apply a rule-based heuristic approach to flag content. While rules are easily customizable and intuitive for humans to interpret,…
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…