LLM Detectors Still Fall Short of Real World: Case of LLM-Generated Short News-Like Posts
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arXiv
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| Format: | Preprint |
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2024
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| author | Gameiro, Henrique Da Silva Kucharavy, Andrei Dolamic, Ljiljana |
| author_facet | Gameiro, Henrique Da Silva Kucharavy, Andrei Dolamic, Ljiljana |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03291 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLM Detectors Still Fall Short of Real World: Case of LLM-Generated Short News-Like Posts Gameiro, Henrique Da Silva Kucharavy, Andrei Dolamic, Ljiljana Computation and Language Artificial Intelligence Cryptography and Security Machine Learning I.2.7; K.6.5 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). |
| title | LLM Detectors Still Fall Short of Real World: Case of LLM-Generated Short News-Like Posts |
| topic | Computation and Language Artificial Intelligence Cryptography and Security Machine Learning I.2.7; K.6.5 |
| url | https://arxiv.org/abs/2409.03291 |