Harmonic Loss Trains Interpretable AI Models

• URL: http://arxiv.org/abs/2502.01628v1
• Date: Mon, 03 Feb 2025 18:57:17 GMT
• Title: Harmonic Loss Trains Interpretable AI Models
• Authors: David D. Baek, Ziming Liu, Riya Tyagi, Max Tegmark,
• Abstract summary: We introduce harmonic loss as an alternative to the standard cross-entropy loss for training neural networks and large language models. We first validate the performance of harmonic models across algorithmic, vision, and language datasets. We demonstrate that models trained with harmonic loss outperform standard models by: (a) enhancing interpretability, (b) requiring less data for generalization, and (c) reducing grokking.
• Score: 13.745919535064429
• License:
• Abstract: In this paper, we introduce **harmonic loss** as an alternative to the standard cross-entropy loss for training neural networks and large language models (LLMs). Harmonic loss enables improved interpretability and faster convergence, owing to its scale invariance and finite convergence point by design, which can be interpreted as a class center. We first validate the performance of harmonic models across algorithmic, vision, and language datasets. Through extensive experiments, we demonstrate that models trained with harmonic loss outperform standard models by: (a) enhancing interpretability, (b) requiring less data for generalization, and (c) reducing grokking. Moreover, we compare a GPT-2 model trained with harmonic loss to the standard GPT-2, illustrating that the harmonic model develops more interpretable representations. Looking forward, we believe harmonic loss has the potential to become a valuable tool in domains with limited data availability or in high-stakes applications where interpretability and reliability are paramount, paving the way for more robust and efficient neural network models.

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