6 papers
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…
A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment
Edward Y. Chang
This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three inde…
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…
Demystifying Long Chain-of-Thought Reasoning in LLMs
Edward Yeo, Yuxuan Tong, Morry Niu +2
Scaling inference compute enhances reasoning in large language models (LLMs), with long chains-of-thought (CoTs) enabling strategies like backtracking and error correction. Reinfor…
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…