
# Generate objections to a claim

This model has the same model parameters as [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), but with an additional soft prompt which has been optimized on the task of generating the objections to a claim, optionally given some example objections to that claim. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) 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](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review.

# Prompt Template

```
[prepended soft prompt][original claim]

Cons:
- [objection 1]
- [objection 2]
...
- [objection n]
- [generated objection]
```

# Dataset

The soft prompt was trained using argument maps scraped from the crowdsourced argument-mapping platform [Kialo](https://kialo.com/).

# Limitations and Biases

The model is a finetuned version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/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.

# Acknowledgements

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.
{"language": ["en"], "license": "apache-2.0", "tags": ["argumentation"], "metrics": ["perplexity"]}
transformers pytorch gpt_neo text-generation argumentation en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# Generate objections to a claim

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 the objections to a claim, optionally given some example objections to that claim. 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.

# Prompt Template



# Dataset

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

# Limitations and Biases

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.

# Acknowledgements

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.
# Generate objections to a claim

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 the objections to a claim, optionally given some example objections to that claim. 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. # Prompt Template # Dataset

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

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. # Acknowledgements

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.
TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Generate objections to a claim

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 the objections to a claim, optionally given some example objections to that claim. 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. # Prompt Template # Dataset

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

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. # Acknowledgements

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.
passage: TAGS
#transformers #pytorch #gpt_neo #text-generation #argumentation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Generate objections to a claim

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 the objections to a claim, optionally given some example objections to that claim. 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.# Prompt Template# Dataset

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

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.# Acknowledgements

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.