Limits of Detecting Text Generated by Large-Scale Language Models
This addresses the risk of AI-generated text in misinformation campaigns, though it is incremental as it builds on existing statistical frameworks.
The paper tackles the problem of detecting text generated by large-scale language models to combat misinformation by formulating it as a hypothesis testing problem, showing that error exponents are bounded by perplexity and characterizing error probabilities under certain assumptions.
Some consider large-scale language models that can generate long and coherent pieces of text as dangerous, since they may be used in misinformation campaigns. Here we formulate large-scale language model output detection as a hypothesis testing problem to classify text as genuine or generated. We show that error exponents for particular language models are bounded in terms of their perplexity, a standard measure of language generation performance. Under the assumption that human language is stationary and ergodic, the formulation is extended from considering specific language models to considering maximum likelihood language models, among the class of k-order Markov approximations; error probabilities are characterized. Some discussion of incorporating semantic side information is also given.