arXiv:2406.12620v3 Announce Type: replace Abstract: Do architectural and training differences influence the way models represent and process language? Traditional similarity metrics tell us whether two models share a similar representational geometry, but they cannot explain why. Here, we propose a new, simple, approach to address this question. This approach maps neural activity in each model layer onto a set of interpretable linguistic features and quantifies how much each of them drives similarities and differences between models. We use this approach to compare 43 language models across 10
The proliferation of various large language models (LLMs) from diverse architectures and training methodologies necessitates better tools for understanding their internal workings and comparative characteristics, pushing research in this direction.
Understanding how different language models 'think alike' is crucial for developing more robust, explainable, and transferable AI systems, impacting future AI design and auditing.
Traditional similarity metrics for language models will be augmented by a more granular, interpretable approach that links neural activity to specific linguistic features, offering deeper insights into model behavior.
- · AI researchers
- · Model developers
- · AI auditing firms
- · Companies investing in explainable AI
- · Developers relying solely on black-box LLMs
- · Companies with proprietary models that resist deep introspection
Improved understanding of representational geometry across different language models.
More targeted and efficient development of new LLM architectures and training methodologies based on feature-level analysis.
Potential for standardized 'linguistic fingerprinting' of AI models, leading to better benchmarks and regulatory oversight.
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Read at arXiv cs.CL