Cat and Mouse -- Can Fake Text Generation Outpace Detector Systems?
This addresses the problem of fake text detection for domains like academic writing and news, but it is incremental as it builds on existing detection methods and focuses on a specific genre.
The study investigated whether fake text generation by large language models can outpace detection systems, finding that while Gemini improved in generating deceptive text over a 0.5 version increase, GPT did not, suggesting reliable detection may remain feasible even as models grow larger.
Large language models can produce convincing "fake text" in domains such as academic writing, product reviews, and political news. Many approaches have been investigated for the detection of artificially generated text. While this may seem to presage an endless "arms race", we note that newer LLMs use ever more parameters, training data, and energy, while relatively simple classifiers demonstrate a good level of detection accuracy with modest resources. To approach the question of whether the models' ability to beat the detectors may therefore reach a plateau, we examine the ability of statistical classifiers to identify "fake text" in the style of classical detective fiction. Over a 0.5 version increase, we found that Gemini showed an increased ability to generate deceptive text, while GPT did not. This suggests that reliable detection of fake text may remain feasible even for ever-larger models, though new model architectures may improve their deceptiveness