Ruixin (Ray) Yang     杨瑞欣

I am a MSCS student at Georgia Tech. I received my BSc degree in Computer Science and Statistics from University of British Columbia in the beautiful Vancouver, Canada.

My research interests are Reliable and Data-Centric AI, with the goal of building safe, socially-aware, and data-efficient Large Language Models (LLMs). Specifically, I am interested in (1) LLM Alignment (2) Addressing the limitations of current LLMs that undermine their reliability, such as privacy leakage and uncertainty miscalibration (3) Data synthesis and selection for LLM training.

Previously, I was a research assistant at Dartmouth College where I had the chance to work with Dr. Ruibo Liu and Prof. Soroush Vosoughi on Tool-augmented Language Models and LLM Alignment.

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We propose Collaborative Calibration, a collaborative approach to elicit, calibrate, and rationalize prediction confidence of LLMs.

Alignment training with data from multi-LLM simulated social interactions, as an efficient, effective, and stable alternative for RLHF.

In this work, we present a novel visual analytical framework to support the analysis of transformer-based generative models.

Competed for Shared Task 0: Generalization and Typologically Diverse Morphological Inflection and achieved the highest performance among all submission in both small and large training conditions.

Credits to Jon Barron's website: source code.