task_categories:text-generation size_categories:1K<n<10K language:en license:cc-by-4.0 region:us
---
license: cc-by-4.0
task_categories:
- text-generation
language:
- en
pretty_name: 'ManaGPT: 4,080 NLP prompts and generated texts'
size_categories:
- 1K<n<10K
---

This dataset includes 4,080 texts that were generated by the [**ManaGPT-1020**](https://huggingface.co/NeuraXenetica/ManaGPT-1020) large language model, in response to particular input sequences.

ManaGPT-1020 is a free, open-source model available for download and use via Hugging Face’s “transformers” Python package. The model is a 1.5-billion-parameter LLM that’s capable of generating text in order to complete a sentence whose first words have been provided via a user-supplied input sequence. The model represents an elaboration of GPT-2 that has been fine-tuned (using Python and TensorFlow) on a specialized English-language corpus of over 509,000 words from the domain of organizational futures studies. In particular, the model has been trained to generate analysis, predictions, and recommendations regarding the emerging role of advanced AI, social robotics, ubiquitous computing, virtual reality, neurocybernetic augmentation, and other “posthumanizing” technologies in organizational life.

In generating the texts, 102 different prompts were used, each of which was employed to generate 20 responses. The 102 input sequences were created by concatenating 12 different "subjects" with 17 different "modal variants," in every possible combination. The subjects included 6 grammatically singular subjects:
- "The workplace of tomorrow"
- "Technological posthumanization"
- "The organizational use of AI"
- "A robotic boss"
- "An artificially intelligent coworker"
- "Business culture within Society 5.0"

Also included were 6 grammatically plural subjects:

- "Social robots"
- "Hybrid human-robotic organizations"
- "Artificially intelligent businesses"
- "The posthumanized workplaces of the future"
- "Cybernetically augmented workers"
- "Organizations in Society 5.0"

For the 6 grammatically singular subjects, the 17 modal variants included one "blank" variant (an empty string) and 16 phrases that lend the input sequence diverse forms of "modal shading," by indicating varying degrees of certainty, probability, predictability, logical necessity, or moral obligation or approbation. These modal variants were:

- ""
- " is"
- " is not"
- " will"
- " will be"
- " may"
- " might never"
- " is likely to"
- " is unlikely to"
- " should"
- " can"
- " cannot"
- " can never"
- " must"
- " must not"
- " is like"
- " will be like"

The variants used with grammatically plural subjects were identical, apart from the fact that the word “is” was changed to “are,” wherever it appeared.

In a small number of cases (only occurring when the empty string "" was used as part of the input sequence), the model failed to generate any output beyond the input sequence itself.