arXiv:2606.08129v1 Announce Type: new Abstract: Large language models (LLMs) differ in architecture, training data, and optimization procedures, yet they may still develop similar internal inference patterns. In this paper, we examine this hypothesis using interaction-based explanations. We find that LLMs often share interaction patterns when predicting the same target token from the same prompt. This consistency is more pronounced among advanced LLMs. Shared interactions also tend to be lower-order and show weaker positive-negative cancellation than non-shared interactions. These results sugg
Ongoing research into LLM interpretability and the push for more robust, understandable AI systems drives this timely investigation into internal consistency.
Understanding cross-LLM consistency offers insights into fundamental AI learning mechanisms, improving reliability and potentially accelerating model development and deployment.
This research suggests a convergence in internal inference patterns among advanced LLMs, hinting at universal cognitive principles emerging across diverse architectures.
- · AI researchers
- · LLM developers
- · Interpretability tool providers
- · AI safety organizations
- · Developers relying solely on architectural divergence for model differentiation
- · Skeptics of emergent intelligence
Increased efforts to identify and leverage these shared inference patterns for more efficient model training and transfer learning.
Development of universal interpretability frameworks applicable across different LLMs, streamlining AI auditing and debugging.
A potential shift towards designing LLMs that explicitly encourage or discourage certain shared interaction patterns, leading to more controllable and predictable AI.
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Read at arXiv cs.AI