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Rodrigo Arias Mallo proposes using asciinema recordings as proof of human authorship for FOSS contributions to Dillo, arguing that LLMs struggle to convincingly generate such recordings.
DetectRL-X is a comprehensive multilingual benchmark for evaluating LLM-generated text detectors across 8 languages and 6 domains, including stress testing with AI-assisted writing operations and perturbations. It reveals strengths and limitations of current detectors in multilingual scenarios.
This paper addresses the degradation of likelihood-based machine-generated text detectors by identifying a Simpson's paradox in token-score aggregation. It proposes a learned local calibration step that significantly improves detection performance across various models and datasets.
Proposes LiSCP, a lightweight stylistic consistency profiling method for robust detection of LLM-generated textual content, focusing on feature stability under adversarial manipulation. Achieves superior performance on in-domain and cross-domain detection with notable robustness.
Andrew Kelley, creator of Zig, argues that LLM-assisted contributions are detectable through distinct mistakes and a 'digital smell,' comparing it to smoking in a non-smoking house.
LLMSniffer is a detection framework that fine-tunes GraphCodeBERT with supervised contrastive learning to distinguish AI-generated code from human-written code, achieving 78% accuracy on GPTSniffer and 94.65% on Whodunit benchmarks. The approach addresses critical challenges in academic integrity and code quality assurance by combining code-structure-aware embeddings with contrastive learning and comment removal preprocessing.