arXiv:2506.07406v3 Announce Type: replace-cross Abstract: Understanding the internal representations of large language models (LLMs) is a central challenge in interpretability research. Existing feature interpretability methods often rely on strong structural assumptions--such as linearity or sparsity--that may not hold in practice. In this work, we introduce InverseScope, an assumption-light and scalable framework for interpreting neural activations via input inversion. Given a target activation, InverseScope characterizes its encoded information by generating natural-language inputs that pro
The increasing complexity and scale of LLMs necessitate more robust interpretability methods to understand their internal workings, which is critical for their reliable deployment.
Improved interpretability fundamentally enhances trust, debuggability, and safety of large language models, accelerating their integration into critical applications and potentially advancing the field of AI alignment.
This new method allows for a more scalable and assumption-light understanding of LLM activations, moving beyond previous limitations and offering deeper insights into how models process information.
- · AI Safety Researchers
- · Large Language Model Developers
- · AI-powered industries
- · Opaque AI systems
- · Traditional, assumption-heavy interpretability methods
More transparent and robust LLMs will be developed, improving their reliability and increasing adoption in sensitive domains.
Enhanced interpretability could accelerate the development of more controllable and aligned AI agents, making them safer for broader societal impact.
A deeper understanding of LLM internal representations might reveal fundamental principles of intelligence, informing the creation of next-generation AI architectures.
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Read at arXiv cs.AI