ABLE constructs model embeddings from gradient-based input attributions, enabling training-free LLM comparison across 239 models with theoretical stability guarantees.
Protect Your Prompts: Protocols for IP Protection in LLM Applications
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abstract
With the rapid adoption of AI in the form of large language models (LLMs), the potential value of carefully engineered prompts has become significant. However, to realize this potential, prompts should be tradable on an open market. Since prompts are, at present, generally economically non-excludable, by virtue of their nature as text, no general competitive market has yet been established. This note discusses two protocols intended to provide protection of prompts, elevating their status as intellectual property, thus confirming the intellectual property rights of prompt engineers, and potentially supporting the flourishing of an open market for LLM prompts.
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cs.CL 1years
2026 1verdicts
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ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding
ABLE constructs model embeddings from gradient-based input attributions, enabling training-free LLM comparison across 239 models with theoretical stability guarantees.