LLM Detectors

LLM detectors aim at detecting text generated by an LLM. They can be categorized into two main types: specific detectors and general detectors. Specific detectors target a particular type of language or context, such as hate speech or spam. In contrast, general detectors aim to identify a broad range of problematic languages, such as misinformation or propaganda. They typically rely on supervised learning, using large labeled datasets to train the models to recognize patterns in the language. General-purpose detectors have shown bad results, but specific-purpose detectors have shown more promising results. This has to be nuanced due to the broad range of effective attacks, especially the paraphrasing attacks, to which all defense techniques are somewhat vulnerable. There are also many other challenges for developing detectors such as the growing numbers of different LLMs (open source or not) being developed and an effective detector that works with many human languages besides English. Mitigation techniques include storing user conversations with an LLM and watermarking (especially cryptographic).