No Model Required: Text Entropy Rate Filtering Mitigates Iterative Fine-Tuning (arxiv.org)

A new study introduces a groundbreaking method for mitigating model collapse during iterative fine-tuning of synthetic data, a challenge that leads to decreased output diversity and increased repetition. This innovative approach leverages the non-parametric Kontoyiannis entropy rate estimator $h_k$, which analyzes raw text using match-length statistics, eliminating the need for model dependencies or external data sources. In experimental trials with the Llama-3.1-8B model, $h_k$-filtering significantly improved text diversity metrics, yielding a 42% increase in unique trigrams and a 19% decrease in repetition, demonstrating superior performance over traditional log-probability methods that depend on model access. The significance of this research lies in its potential to transform how the AI/ML community approaches synthetic data generation and fine-tuning processes. By utilizing information theory principles, the study presents an efficient mechanism for preserving diversity in generated outputs without relying on complex models or real human data. This advancement could pave the way for more robust multi-agent systems, enhancing their capacity to maintain variability and adaptability in applications ranging from natural language processing to generative design.