Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models

• URL: http://arxiv.org/abs/2401.17139v2
• Date: Mon, 14 Oct 2024 04:36:09 GMT
• Title: Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models
• Authors: Lai Wei, Zhiquan Tan, Chenghai Li, Jindong Wang, Weiran Huang,
• Abstract summary: We introduce a rank-based metric, Diff-eRank, grounded in information theory and geometry principles. For language models, our results show that Diff-eRank increases with model size and correlates well with conventional metrics such as loss and accuracy. In the multi-modal context, we propose an alignment evaluation method based on the eRank, and verify that contemporary multi-modal LLMs exhibit strong alignment performance based on our method.
• Score: 10.677971531050611
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have transformed natural language processing and extended their powerful capabilities to multi-modal domains. As LLMs continue to advance, it is crucial to develop diverse and appropriate metrics for their evaluation. In this paper, we introduce a novel rank-based metric, Diff-eRank, grounded in information theory and geometry principles. Diff-eRank assesses LLMs by analyzing their hidden representations, providing a quantitative measure of how efficiently they eliminate redundant information during training. We demonstrate the applicability of Diff-eRank in both single-modal (e.g., language) and multi-modal settings. For language models, our results show that Diff-eRank increases with model size and correlates well with conventional metrics such as loss and accuracy. In the multi-modal context, we propose an alignment evaluation method based on the eRank, and verify that contemporary multi-modal LLMs exhibit strong alignment performance based on our method. Our code is publicly available at https://github.com/waltonfuture/Diff-eRank.

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