LLM Detectors Still Fall Short of Real World: Case of LLM-Generated Short News-Like Posts
<|reference_start|>LLM Detectors Still Fall Short of Real World: Case of LLM-Generated Short News-Like Posts: With the emergence of widely available powerful LLMs, disinformation generated by large Language Models (LLMs) has become a major concern. Historically, LLM detectors have been touted as a solution, but their effectiveness in the real world is still to be proven. In this paper, we focus on an important setting in information operations -- short news-like posts generated by moderately sophisticated attackers.   We demonstrate that existing LLM detectors, whether zero-shot or purpose-trained, are not ready for real-world use in that setting. All tested zero-shot detectors perform inconsistently with prior benchmarks and are highly vulnerable to sampling temperature increase, a trivial attack absent from recent benchmarks. A purpose-trained detector generalizing across LLMs and unseen attacks can be developed, but it fails to generalize to new human-written texts.   We argue that the former indicates domain-specific benchmarking is needed, while the latter suggests a trade-off between the adversarial evasion resilience and overfitting to the reference human text, with both needing evaluation in benchmarks and currently absent. We believe this suggests a re-consideration of current LLM detector benchmarking approaches and provides a dynamically extensible benchmark to allow it (https://github.com/Reliable-Information-Lab-HEVS/benchmark_llm_texts_detection).<|reference_end|>
@article{gameiro2024llm,
    title={LLM Detectors Still Fall Short of Real World: Case of LLM-Generated
  Short News-Like Posts},
    author={Henrique Da Silva Gameiro, Andrei Kucharavy, Ljiljana Dolamic},
    journal={arXiv preprint arXiv:2409.03291},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03291},
    primaryClass={cs.CL cs.AI cs.CR cs.LG}
}