3 citations · 5 across the 5 of their papers we have counts for
5 papers
From Bias to Balance: Detecting Facial Expression Recognition Biases in Large Multimodal Foundation Models
Kaylee Chhua, Zhoujinyi Wen, Vedant Hathalia +2
This study addresses the racial biases in facial expression recognition (FER) systems within Large Multimodal Foundation Models (LMFMs). Despite advances in deep learning and the a…
Enhancing Depression Diagnosis with Chain-of-Thought Prompting
Elysia Shi, Adithri Manda, London Chowdhury +3
When using AI to detect signs of depressive disorder, AI models habitually draw preemptive conclusions. We theorize that using chain-of-thought (CoT) prompting to evaluate Patient…
Chain-of-Thought Augmentation with Logit Contrast for Enhanced Reasoning in Language Models
Jay Shim, Grant Kruttschnitt, Alyssa Ma +5
Rapidly increasing model scales coupled with steering methods such as chain-of-thought prompting have led to drastic improvements in language model reasoning. At the same time, mod…
Question-Analysis Prompting Improves LLM Performance in Reasoning Tasks
Dharunish Yugeswardeenoo, Kevin Zhu, Sean O'Brien
Although LLMs have the potential to transform many fields, they still underperform humans in reasoning tasks. Existing methods induce the model to produce step-by-step calculations…
Atom-by-atom protein generation and beyond with language models
Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik
Protein language models learn powerful representations directly from sequences of amino acids. However, they are constrained to generate proteins with only the set of amino acids r…