This model has the same model parameters as gpt-neo-2.7B, but with an additional soft prompt which has been optimized on the task of generating a sequence of claims (a 'chain of reasoning') that joins one claim to another. It was trained as part of a University of Melbourne research project evaluating how large language models can best be optimized to perform argumentative reasoning tasks.

Code used for optimization and evaluation can be found in the project GitHub repository. A paper reporting on model evaluation is currently under review.

[prepended soft prompt][start claim] -> [end claim]

Answer: [start claim] -> [generated intermediate claim 1] -> ... -> [generated intermediate claim n] -> [end claim]

The soft prompt was trained using argument maps scraped from the crowdsourced argument-mapping platform Kialo.

The model is a finetuned version of gpt-neo-2.7B, so likely has many of the same limitations and biases. Additionally, note that while the goal of the model is to produce coherent and valid reasoning, many generated model outputs will be illogical or nonsensical and should not be relied upon.

This research was funded by the Australian Department of Defence and the Office of National Intelligence under the AI for Decision Making Program, delivered in partnership with the Defence Science Institute in Victoria, Australia.