arXiv:2605.23080v1 Announce Type: new Abstract: Feature attribution methods promise to identify which input features matter for a model output. In generative language models, however, it is often unclear what should count as a feature in the first place. In autoregressive language models, earlier generated tokens are both outputs of the model and inputs to later predictions. In diffusion language models, generation proceeds through iterative denoising or unmasking rather than fixed left-to-right prediction, so local explanation may target a state of diffusion rather than a next token. We argue
The rapid advancement and increasing complexity of generative language models necessitate more robust and transparent attribution methods, aligning with growing demands for explainable AI.
Improved feature attribution is crucial for debugging, auditing, and building trust in generative AI, particularly as these models become more integrated into critical applications.
The development of a clearer 'Attribution Contract' provides a standardized framework for understanding and evaluating how different input features influence generative language model outputs.
- · AI developers
- · AI auditors
- · Regulatory bodies
- · Enterprises adopting AI
- · Developers of 'black box' AI models
- · Companies unable to explain AI outputs
More reliable and transparent generative AI models will emerge, fostering greater adoption in sensitive domains.
New tools and standards for explainable AI will be developed, accelerating research and practical applications in this field.
Increased societal trust in AI systems could lead to broader integration of autonomous AI agents in various industries without significant regulatory friction.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG