Stylometric features and tree-based classifiers separate human-written Wikipedia summaries from LLM-generated texts with high cross-validated accuracy on a new seven-class benchmark, though performance drops on other text types.
GPT4All: An Ecosystem of Open Source Compressed Language Models
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abstract
Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks. The accessibility of these models has lagged behind their performance. State-of-the-art LLMs require costly infrastructure; are only accessible via rate-limited, geo-locked, and censored web interfaces; and lack publicly available code and technical reports. In this paper, we tell the story of GPT4All, a popular open source repository that aims to democratize access to LLMs. We outline the technical details of the original GPT4All model family, as well as the evolution of the GPT4All project from a single model into a fully fledged open source ecosystem. It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem.
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Stylometry recognizes human and LLM-generated texts in short samples
Stylometric features and tree-based classifiers separate human-written Wikipedia summaries from LLM-generated texts with high cross-validated accuracy on a new seven-class benchmark, though performance drops on other text types.