arXiv:2605.22897v1 Announce Type: new Abstract: A persistent challenge in machine learning for scientific applications is jointly achieving prediction and understanding. Statistical models excel on structured data but operate as black boxes, while existing interpretability methods are largely inspective: they answer "which features matter?" but do not articulate how features interact or refine explanations iteratively alongside human understanding. Asking an LLM to predict the target directly forces it to search the entire output space; we instead anchor predictions with a base model and ask t
The increasing complexity of AI models, particularly LLMs, and the growing demand for explainability in scientific and regulatory contexts, drive innovation in interpretability methods.
This development allows for deeper understanding of machine learning predictions in scientific applications, moving beyond 'what' to 'how' features interact, which is crucial for trust and refinement of AI models.
Machine learning interpretability shifts from merely identifying important features to inferring underlying causal mechanisms using LLMs, offering a more nuanced and interactive explanation process.
- · AI researchers and scientists
- · Developers of interpretability tools
- · Industries requiring explainable AI (e.g., healthcare, finance)
- · LLM developers
- · Black-box AI systems without interpretability
Improved understanding and trust in AI-driven scientific discoveries and predictions.
Accelerated integration of AI into sensitive scientific and regulatory domains due to enhanced explainability.
Potential for LLMs to become a standard component in the scientific discovery pipeline, guiding hypothesis generation and mechanism inference.
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Read at arXiv cs.LG