arXiv:2607.00023v1 Announce Type: cross Abstract: Dense sentence embeddings are fundamental to modern Retrieval-Augmented Generation (RAG) systems but suffer from a lack of interpretability due to feature superposition. This opacity hinders the alignment of retrieval processes with human intent, as the entangled representations are difficult to analyze or control. In this work, we propose a method to disentangle the dense representations of sentence transformers (e.g., E5) into human-interpretable concepts using Top-k Sparse Autoencoders (SAEs). We demonstrate that these disentangled features
The increasing complexity and opacity of modern AI models, particularly in RAG systems, demand novel approaches for interpretability to enhance alignment with human intent.
Improving the interpretability of sentence embeddings is crucial for developing more reliable, controllable, and human-aligned AI agents and retrieval systems.
This research introduces a method to disentangle opaque sentence embeddings into interpretable human concepts, potentially making RAG systems more transparent and auditable.
- · AI developers
- · RAG system integrators
- · AI ethics and safety researchers
- · Developers relying solely on black-box AI models
- · Systems with high interpretability requirements but lacking suitable tools
Sentence embeddings become more interpretable, allowing for better debugging and fine-tuning of RAG systems.
Increased trust and adoption of AI systems due to enhanced transparency and alignment with human conceptual frameworks.
New tooling and standards emerge for interpretability in AI, potentially influencing regulatory frameworks for AI safety and trustworthiness.
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Read at arXiv cs.AI