Perplexity-based detectors are the most robust to unknown AI code generators, but they are less accurate and slower than supervised detectors, working best on C/C++ and large code snippets.
Copyleft for Alleviating AIGC Copyright Dilemma: What-if Analysis, Public Perception and Implications
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
As AIGC has impacted our society profoundly in the past years, ethical issues have received tremendous attention. The most urgent one is the AIGC copyright dilemma, which can immensely stifle the development of AIGC and greatly cost the entire society. Given the complexity of AIGC copyright governance and the fact that no perfect solution currently exists, previous work advocated copyleft on AI governance but without substantive analysis. In this paper, we take a step further to explore the feasibility of copyleft to alleviate the AIGC copyright dilemma. We conduct a mixed-methods study from two aspects: qualitatively, we use a formal what-if analysis to clarify the dilemma and provide case studies to show the feasibility of copyleft; quantitatively, we perform a carefully designed survey to find out how the public feels about copylefting AIGC. The key findings include: a) people generally perceive the dilemma, b) they prefer to use authorized AIGC under loose restriction, and c) they are positive to copyleft in AIGC and willing to use it in the future.
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cs.SE 1years
2024 1verdicts
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One Size Does Not Fit All: Investigating Efficacy of Perplexity in Detecting LLM-Generated Code
Perplexity-based detectors are the most robust to unknown AI code generators, but they are less accurate and slower than supervised detectors, working best on C/C++ and large code snippets.