The Necessity of Imperfection: Reversing Model Collapse via Simulating Cognitive Boundedness

This article, sourced from ArXiv, suggests a novel approach to address model collapse in large language models (LLMs). The core idea revolves around introducing imperfections, or cognitive boundedness, into the training process. This is a potentially significant contribution as model collapse is a known challenge in LLM development. The research likely explores methods to simulate human-like limitations in LLMs to improve their robustness and prevent catastrophic forgetting or degradation of performance.

• •Addresses model collapse in LLMs.
• •Proposes simulating cognitive boundedness.
• •Aims to improve LLM robustness.