AI-Brown

AI-Brown is a generated, annotated, multi-genre corpus of English texts produced by large language models (LLMs).

Modeled on the BE21 Corpus¹⁾ — a modern implementation of the original Brown Corpus—AI-Brown was created to replicate its structure, genre diversity, and linguistic richness, enabling systematic comparisons between human and machine-generated English texts. The corpus comprises outputs from 13 frontier LLMs developed by OpenAI, Anthropic, Meta, Alphabet, and DeepSeek. Each model was prompted using the first 500 words of BE21 text samples, with the remaining portion reserved as human-authored reference material, ensuring genre-aligned and topically consistent comparisons. Like BE21, AI-Brown spans a wide range of contemporary English genres. All generated texts are tokenized, lemmatized, and annotated morphologically and syntactically using the Universal Dependencies framework, and are provided in both plain text and CoNLL-U formats. AI-Brown is a large-scale English LLM-generated corpus explicitly designed for cross-model and human-machine linguistic analysis.

The original reference BE21 Corpus was available in vertical format via the Czech National Corpus infrastructure. The preprocessing pipeline included several steps to prepare the data for prompt-based generation. Clean texts and metadata were extracted from the verticals, and structural tags were aligned with the Czech corpus format to ensure cross-linguistic consistency.

Each BE21 text sample was split into two parts to support controlled generation:

• Prompt portion: The first 500 words (including punctuation) served as generation prompts

• Reference portion: The remaining text (approximately 1,500 words) provided human-authored comparison material

This segmentation ensured that models received sufficient context for meaningful text generation while preserving a substantial portion of reference text for evaluation. A 500-word prompt fits comfortably within the input limits of older models (e.g., davinci-002, which has a 2,049-token context window), using roughly 670 tokens.

To maintain comparability with the Czech AI corpus AI-Koditex and avoid over-representation, we selected written texts only, limiting the sample to one excerpt per source text. The final AI-Brown dataset contains 500 samples, matching the original structure of BE21.

For each model, we generated two versions of each corpus: one using temperature 0 (deterministic generation) and one using temperature 1 (stochastic generation). However, we encountered mode collapse with the oldest model (davinci-002) at zero temperature, resulting in constant repetition of identical sentences.

For base models operating in completion mode (davinci-002, GPT-3.5-turbo, Meta-Llama-3.1-405B), we used only the first portion of each source text as input, allowing the models to function as traditional language models for text prediction. For instruction-tuned models, we employed minimal system prompts requesting long continuation of given text. Without such prompts, models' default helpful assistant persona emerged who typically attempted to analyze, summarize, or answer questions within the source text rather than continuing it. We used the following system prompt: Please continue the text in the same manner and style, ensuring it contains at least five thousand words. The text does not need to be factually correct, but please make sure it fits stylistically.

To ensure reproducibility, we used random seed 42 for all OpenAI API calls. Unfortunately, other providers do not offer comparable deterministic generation options. Llama generations used 16-bit floating-point quantization (the highest available quality).

API responses were preserved in their entirety, including token probabilities and alternative tokens when available, to enable future analysis of generation uncertainty and model confidence.

Texts that were too short were removed. For instruction-tuned models, the original phrases such as “I'd be happy to continue” were also removed.

We used Universal Dependencies for annotation, as UDPipe represents the state of the art for multi-level linguistic processing (including tokenization, lemmatization, syntax, and morphology). The resulting annotation is in the CoNLL-U format, which is a widely adopted standard compatible with most modern NLP tools.