aiqbench analysis
This research advances LLM authorship detection by shifting focus from superficial linguistic patterns to the underlying reasoning structures embedded in generated text. By utilizing a graph neural network on argument mining pipelines, the method significantly improves robustness against obfuscation techniques like paraphrasing and maintains accuracy when facing unseen model versions. This development addresses critical challenges for content verification as generative models continue to evolve rapidly across diverse applications.
Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution
arXiv:2607.14905v2 Announce Type: replace-cross Abstract: Given the current trend to employ large language models (LLMs) in almost any imaginable context, LLM-generated text detection and authorship attribution have become a pressing issue. Prior work has primarily focused on surface-level linguistic features, an approach shown to be susceptible to paraphrasing and other obfuscation techniques. In this paper, we go beyond the linguistic surface, extracting and analysing reasoning structures in LLM-generated texts with the goal of capturing more complex signals of LLM authorship. We propose a graph neural network approach that leverages reasoning graphs extracted by an argument mining pipeline, demonstrating improved robustness and generalisation over a traditional Longformer baseline. Our approach outperforms the baseline by up to 27 percentage points under the obfuscation attacks such as paraphrasing and backtranslation, and 19 percentage points when evaluated on the texts generated by the unseen model versions, simulating real-world conditions in which new LLM versions are continuously released.
Abstract:Given the current trend to employ large language models (LLMs) in almost any imaginable context, LLM-generated text detection and authorship attribution have become a pressing issue. Prior work has primarily focused on surface-level linguistic features, an approach shown to be susceptible to paraphrasing and other obfuscation techniques. In this paper, we go beyond the linguistic surface, extracting and analysing reasoning structures in LLM-generated texts with the goal of capturing more complex signals of LLM authorship. We propose a graph neural network approach that leverages reasoning graphs extracted by an argument mining pipeline, demonstrating improved robustness and generalisation over a traditional Longformer baseline. Our approach outperforms the baseline by up to 27 percentage points under the obfuscation attacks such as paraphrasing and backtranslation, and 19 percentage points when evaluated on the texts generated by the unseen model versions, simulating real-world conditions in which new LLM versions are continuously released.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2607.14905 [cs.CL]
(or arXiv:2607.14905v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.14905 Focus to learn more arXiv-issued DOI via DataCite
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