LLM-generated Text Detection
5 papers with code • 0 benchmarks • 1 datasets
Classifying human-written and LLM-generated texts.
Benchmarks
These leaderboards are used to track progress in LLM-generated Text Detection
WaterBench
Most implemented papers
Red Teaming Language Model Detectors with Language Models
shizhouxing/Attack-LM-Detectors
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The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users.
OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples
ryuryukke/OUTFOX
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Experiments in the domain of student essays show that the proposed detector improves the detection performance on the attacker-generated texts by up to +41. 3 points F1-score.
A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
In this survey, we collate recent research breakthroughs in this area and underscore the pressing need to bolster detector research.
How You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text Detection
Furthermore, our analysis indicates that the high instruction-following ability of LLMs fosters the large impact of such constraints on detection performance.
Exploring Naive Approaches to Tell Apart LLMs Productions from Human-written Text
Powerful Large Language Models (large LMs or LLMs) such as BERT and GPT are making the task of detecting machine-generated text more and more prominent and crucial to minimize threats posed by text generation models misuse.
