LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection
The widespread accessibility of large language models (LLMs) to the general
public has significantly amplified the dissemination of machine-generated texts
(MGTs). Advancements in prompt manipulation have exacerbated the difficulty in
discerning the origin of a text (human-authored vs machinegenerated). This
raises concerns regarding the potential misuse of MGTs, particularly within
educational and academic domains. In this paper, we present
LLM-DetectAIve -- a system designed for fine-grained MGT detection.
It is able to classify texts into four categories: human-written,
machine-generated, machine-written machine-humanized, and human-written
machine-polished. Contrary to previous MGT detectors that perform binary
classification, introducing two additional categories in LLM-DetectiAIve offers
insights into the varying degrees of LLM intervention during the text creation.
This might be useful in some domains like education, where any LLM intervention
is usually prohibited. Experiments show that LLM-DetectAIve can effectively
identify the authorship of textual content, proving its usefulness in enhancing
integrity in education, academia, and other domains. LLM-DetectAIve is publicly
accessible at https://huggingface.co/spaces/raj-tomar001/MGT-New. The video
describing our system is available at https://youtu.be/E8eT_bE7k8c.
66b58f2783425904fa9d34eb
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LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection
The widespread accessibility of large language models (LLMs) to the general
public has significantly amplified the dissemination of machine-generated texts
(MGTs). Advancements in prompt manipulation have exacerbated the difficulty in
discerning the origin of a text (human-authored vs machinegenerated). This
raises concerns regarding the potential misuse of MGTs, particularly within
educational and academic domains. In this paper, we present
LLM-DetectAIve -- a system designed for fine-grained MGT detection.
It is able to classify texts into four categories: human-written,
machine-generated, machine-written machine-humanized, and human-written
machine-polished. Contrary to previous MGT detectors that perform binary
classification, introducing two additional categories in LLM-DetectiAIve offers
insights into the varying degrees of LLM intervention during the text creation.
This might be useful in some domains like education, where any LLM intervention
is usually prohibited. Experiments show that LLM-DetectAIve can effectively
identify the authorship of textual content, proving its usefulness in enhancing
integrity in education, academia, and other domains. LLM-DetectAIve is publicly
accessible at https://huggingface.co/spaces/raj-tomar001/MGT-New. The video
describing our system is available at https://youtu.be/E8eT_bE7k8c.
LLM-DetectAIve is a system that classifies texts as human-written, machine-generated, machine-written machine-humanized, and human-written machine-polished to enhance integrity in domains like education and academia.
LLM-DetectAIve large language models machine-generated texts prompt manipulation MGT detection