3 citations · 3 across the 1 of their papers we have counts for
6 papers · 1 filter
AGI Requires a Coordination Layer on Top of Pattern Repositories
Edward Y. Chang
In this paper we argue that influential critiques dismissing Large Language Models (LLMs) as a dead end for AGI misidentify the bottleneck: they confuse the ocean with the net. Pat…
Unlocking the Wisdom of Large Language Models: An Introduction to The Path to Artificial General Intelligence
Edward Y. Chang
This booklet, Unlocking the Wisdom of Multi-LLM Collaborative Intelligence, serves as an accessible introduction to the full volume The Path to Artificial General Intelligence. Thr…
EVINCE: Optimizing Multi-LLM Dialogues Using Conditional Statistics and Information Theory
Edward Y. Chang
EVINCE (Entropy and Variation IN Conditional Exchanges) is a novel framework for optimizing multi-LLM dialogues using conditional statistics and information theory. It addresses li…
Uncovering Biases with Reflective Large Language Models
Edward Y. Chang
Biases and errors in human-labeled data present significant challenges for machine learning, especially in supervised learning reliant on potentially flawed ground truth data. Thes…
Ensuring Ground Truth Accuracy in Healthcare with the EVINCE framework
Edward Y. Chang
Misdiagnosis is a significant issue in healthcare, leading to harmful consequences for patients. The propagation of mislabeled data through machine learning models into clinical pr…
SocraSynth: Multi-LLM Reasoning with Conditional Statistics
Edward Y. Chang
Large language models (LLMs), while promising, face criticisms for biases, hallucinations, and a lack of reasoning capability. This paper introduces SocraSynth, a multi-LLM agent r…