Keskeiset käsitteet
TEXTMACHINA is a modular and extensible Python framework designed to aid in the creation of high-quality, unbiased datasets for various machine-generated text (MGT) tasks, such as detection, attribution, boundary detection, and mixcase detection.
Tiivistelmä
TEXTMACHINA is a comprehensive framework that addresses the challenges in generating high-quality MGT datasets. It provides the following key features:
Model Providers: TEXTMACHINA integrates with various large language model (LLM) providers, including Anthropic, Cohere, OpenAI, Azure OpenAI, Google Vertex AI, Amazon Bedrock, AI21, and HuggingFace models, allowing users to generate text using the latest and most advanced LLMs.
Extractors: TEXTMACHINA offers a set of extractors that can be used to fill prompt templates with information from human text datasets, such as titles, summaries, entities, and word/sentence prefixes. This helps guide the LLMs to generate text that is relevant to the target domain and style.
Constrainers: TEXTMACHINA includes constrainers that can automatically infer decoding parameters, such as temperature, to ensure the generated text matches the characteristics of the human text dataset.
Post-processing: TEXTMACHINA applies a set of post-processing steps to the generated and human texts, addressing common biases and artifacts, such as language, encoding, disclosure, and length biases.
Metrics: TEXTMACHINA provides a set of metrics to assess the quality and task difficulty of the generated datasets, including MAUVE, text perplexity, repetition and diversity, and classification model performance.
Usability: TEXTMACHINA offers a user-friendly command-line interface and programmatic API to generate and explore datasets, making it easy for researchers and practitioners to build high-quality MGT datasets for their specific needs.
TEXTMACHINA has been successfully used to create datasets for various MGT-related tasks, including the AuTexTification and IberAuTexTification shared tasks, which have been downloaded thousands of times and used by over one hundred teams to develop robust MGT detection and attribution models.
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