diff --git a/_pages/about.md b/_pages/about.md index f45df7bb908f7..bc80c63ad8a09 100644 --- a/_pages/about.md +++ b/_pages/about.md @@ -15,7 +15,7 @@ redirect_from:

Welcome to Persist (Pervasive Intelligent Systems) Lab! We are a research group in the Department of Computer Science at Dartmouth College. -Our mission is the seamless integration of intelligence into our daily lives, enabling us to better decipher the intricacies of the virtual and physical worlds surrounding us. We are dedicated to crafting innovative solutions at the intersection of human-AI interaction, natural language processing, and machine learning. Our goal is to decode vast volumes of complex data, ranging from Electronic Health Records (EHR) to social media posts and sensor data, to enhance decision-making processes. +Our mission is the seamless integration of intelligence into our daily lives, enabling us to better decipher the intricacies of the virtual and physical worlds surrounding us. We develop innovative solutions at the intersection of human-AI interaction, natural language processing, and machine learning. Our goal is to decode vast volumes of complex data, ranging from Electronic Health Records (EHR) to social media posts and sensor data, to enhance decision-making processes.

@@ -38,6 +38,13 @@ Our mission is the seamless integration of intelligence into our daily lives, en We successfully wrapped the Reliable Evaluation of LLMs for Factual Information (REAL-Info), at ICWSM-2024! Thanks to my wonderful co-organizers, students, and all our participants! Special thanks to Dr. Munmun De Choudhury for her amazing keynote!
+21 April, 2024
+ Check out our paper Do LLMs Find Human Answers To Fact-Driven Questions Perplexing? A Case Study on Reddit, at Reliable Evaluation of LLMs for Factual Information (REAL-Info) workshop, co-located with ICWSM-2024. +
+ + + + 8 March, 2024
We have posted two new pre-prints on arXiv! (1) Mad Libs Are All You Need: Augmenting Cross-Domain Document-Level Event Argument Data (2) Scope of Large Language Models for Mining Emerging Opinions in Online Health Discourse. Check them out!