arXiv:2602.11852v2 Announce Type: replace-cross Abstract: While state-of-the-art language models (LMs) surpass most humans in certain domains, their reasoning remains largely opaque, reducing trust and increasing the risk of deception and hallucination. We introduce the Prototype Transformer (ProtoT), an autoregressive LM architecture that replaces the quadratic-cost self-attention module of the Transformer with a linear-cost module based on prototypes, which are learned parameter vectors. In ProtoT, prototypes create communication channels that aggregate contextual information at different ti
The increasing scale and deployment of large language models necessitate solutions for interpretability, trust, and hallucination reduction, pushing research towards novel architectural designs.
This development offers a potential path to more transparent and reliable AI, addressing critical trust barriers that hinder wider adoption and deployment in sensitive applications.
LM architectures could shift from purely opaque self-attention mechanisms to more interpretable, prototype-based systems, potentially impacting model development and evaluation paradigms.
- · AI safety researchers
- · Developers of critical AI applications
- · Users requiring transparent AI systems
- · Explainable AI (XAI) platforms
- · Opaque black-box AI systems
- · Companies relying solely on scale without interpretability
The Prototype Transformer (ProtoT) introduces a novel, interpretable architecture that replaces the quadratic-cost self-attention of traditional Transformers with a linear-cost, prototype-based module.
Improved interpretability and reduced hallucination in LMs could accelerate their adoption in high-stakes domains such as healthcare, finance, and legal services, where trust is paramount.
A shift towards interpretable-by-design AI architectures could fundamentally alter regulatory landscapes, potentially leading to new compliance standards for transparency in AI systems.
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Read at arXiv cs.CL