aiqbench analysis
SemTrace introduces a novel method for detecting LLM exposure to protected documents by embedding semantic signatures within factual propositions rather than relying on surface-level token bias. This approach enables model-agnostic provenance tracking, allowing document owners to verify whether their manuscripts influenced downstream generations without altering the model's core architecture or independent evaluation capabilities. The technique addresses critical copyright and data leakage concerns as LLMs increasingly process proprietary text in production environments.
SemTrace: Source-Grounded Semantic Signatures for Tracing LLM Exposure to Protected Documents
arXiv:2608.29575v1 Announce Type: new Abstract: Large language models are increasingly used to read documents and produce downstream text, creating a provenance problem when the document owner cannot control or inspect the model that performs the generation. We introduce SemTrace, a source-grounded semantic watermark for detecting whether a generated review was influenced by a known protected manuscript copy. Rather than biasing token probabilities or imposing surface-form patterns, SemTrace constructs a document-specific binary signature from factual propositions that are directly supported by the manuscript itself. A protected PDF invisibly carries a content contract that selects one fact from each binary pair and asks an instruction-following reviewer to express those facts in fixed review slots without changing its independent evaluation. A frozen natural language inference model then decodes the resulting semantic evidence with explicit erasures and scores the recovered bits against the codeword assigned to that copy. This design targets model-agnostic, assigned-copy exposure detection while keeping the watermark semantically tied to the source document.
Abstract:Large language models are increasingly used to read documents and produce downstream text, creating a provenance problem when the document owner cannot control or inspect the model that performs the generation. We introduce SemTrace, a source-grounded semantic watermark for detecting whether a generated review was influenced by a known protected manuscript copy. Rather than biasing token probabilities or imposing surface-form patterns, SemTrace constructs a document-specific binary signature from factual propositions that are directly supported by the manuscript itself. A protected PDF invisibly carries a content contract that selects one fact from each binary pair and asks an instruction-following reviewer to express those facts in fixed review slots without changing its independent evaluation. A frozen natural language inference model then decodes the resulting semantic evidence with explicit erasures and scores the recovered bits against the codeword assigned to that copy. This design targets model-agnostic, assigned-copy exposure detection while keeping the watermark semantically tied to the source document.
Computation and Language (cs.CL)
arXiv:2608.29575 [cs.CL]
(or arXiv:2608.29575v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.29575 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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