4 citations · 7 across the 6 of their papers we have counts for
6 papers
Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity
Rheeya Uppaal, Apratim Dey, Yiting He +2
Recent alignment algorithms such as direct preference optimization (DPO) have been developed to improve the safety of large language models (LLMs) by training these models to match…
How Useful is Continued Pre-Training for Generative Unsupervised Domain Adaptation?
Rheeya Uppaal, Yixuan Li, Junjie Hu
Recent breakthroughs in scale have enabled the emergence of powerful generative language models, and the ability to fine-tune these models on various tasks by casting them into pro…
Evolving Domain Adaptation of Pretrained Language Models for Text Classification
Yun-Shiuan Chuang, Yi Wu, Dhruv Gupta +7
Adapting pre-trained language models (PLMs) for time-series text classification amidst evolving domain shifts (EDS) is critical for maintaining accuracy in applications like stance…
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection
Rheeya Uppaal, Junjie Hu, Yixuan Li
Out-of-distribution (OOD) detection is a critical task for reliable predictions over text. Fine-tuning with pre-trained language models has been a de facto procedure to derive OOD…
Long Document Summarization in a Low Resource Setting using Pretrained Language Models
Ahsaas Bajaj, Pavitra Dangati, Kalpesh Krishna +7
Abstractive summarization is the task of compressing a long document into a coherent short document while retaining salient information. Modern abstractive summarization methods ar…
Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)
Bhanu Pratap Singh, Iman Deznabi, Bharath Narasimhan +4
Missing values, irregularly collected samples, and multi-resolution signals commonly occur in multivariate time series data, making predictive tasks difficult. These challenges are…