arXiv:2605.22377v1 Announce Type: new Abstract: Transformer-based language models such as BERT having 110M+ parameters have revolutionized natural language understanding, yet their internal mechanisms remain largely opaque to researchers and practitioners. Traditional attention-based interpretability methods often emphasize structurally important but semantically weak tokens such as punctuation marks rather than meaningful semantic relationships. This work introduces a lightweight and model-agnostic framework for quantifying token-level representational importance using hidden-state activation
The rapid advancement and widespread deployment of large language models necessitate improved interpretability to ensure reliability and trust, especially as these models are integrated into critical applications.
Understanding the internal mechanisms of AI models is crucial for debugging, improving performance, mitigating biases, and ensuring ethical deployment, moving AI beyond a black box.
This framework offers a more semantically powerful method for token-level interpretability, potentially shifting research away from structurally important but less meaningful tokens like punctuation.
- · AI researchers
- · AI developers
- · Companies deploying LLMs in sensitive areas
- · Users of AI systems
- · Developers reliant on ad-hoc interpretability methods
- · Techniques that overemphasize structural tokens
Improved interpretability tools lead to more robust and reliable AI models, enhancing trust in their applications.
Greater understanding of model internals could accelerate breakthroughs in AI architecture design and reduce unforeseen failure modes.
Ethical AI guidelines may evolve to mandate specific levels of model explainability, impacting regulatory landscapes and deployment standards across industries.
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Read at arXiv cs.LG