Papers with LLM detection
The Two Paradigms of LLM Detection: Authorship Attribution vs Authorship Verification (2025.findings-acl)
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| Challenge: | Existing methods for detecting texts generated by large language models are disputed . authors argue that there are limitations in the current technology . |
| Approach: | They propose to make LLM detectors robust against domain shifts and build benchmarks . they argue that the limitations lie elsewhere, and open the realm of authorship analysis technology . |
| Outcome: | The proposed method systematically analyzes the benchmarks and validates it using state-of-the-art detectors. |
On the Generalization of Training-based ChatGPT Detection Methods (2024.findings-emnlp)
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| Challenge: | Existing studies show that training-based methods are ineffective to detect LLM generated texts from unseen tasks or topics which are not collected during training. |
| Approach: | They propose to train classification models to distinguish LLMs from human texts by a distribution shift caused by prompts, text lengths, topics, and language tasks. |
| Outcome: | The proposed methods can detect LLMs from black-box models, but they suffer from distribution shifts due to a wide range of factors, including prompts, text lengths, topics, and language tasks. |
From Text to Source: Results in Detecting Large Language Model-Generated Content (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) generate human-like text, but have ethical and misuse concerns. |
| Approach: | They evaluate whether a classifier trained to distinguish between source and target LLMs can detect text from an LLM without further training. |
| Outcome: | The proposed method detects text from target LLMs without further training. |
How You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text Detection (2024.findings-emnlp)
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| Challenge: | Recent studies have presented LLM-generated-text detectors with promising performance, but they do not cover such diverse instruction patterns when creating datasets for LLM detection. |
| Approach: | They propose to use task-oriented constraints that would naturally be included in an instruction and are not related to detection-evasion to create detectors with large variances in detection performance. |
| Outcome: | The proposed detectors have a large variance in detection performance on student essay writing with task-oriented constraints, and the standard deviation is significantly larger than that on texts generated by the constraint with such a constraint. |