AI Research Presents Large Language Model that Answers Philosophical Questions in Voice of Specific Philosopher

Are AI Models Close to Speaking in the Voice of a Philosopher?

In recent years, Artificial Intelligence has made leaps and bounds in its development, allowing it to outperform humans in a wide range of tasks. But can AI models be taught to write philosophical essays indistinguishable from those written by actual philosophers? To answer this question, researchers from the University of California-Riverside, École Normale Supérieure (ECN) in Paris, and Ludwig-Maximilians-Universität München created a large language model that can respond to philosophical queries in a manner that is very similar to that of a particular philosopher.

Introducing GPT-3

GPT-3, or the third-generation Generative Pre-Trained Transformer, is an autoregressive language model that uses deep learning to generate texts. The model’s foundation lies in using sophisticated and robust statistical algorithms on the input text prompt to predict the following word in a sentence. To do this, the language model analyses a massive corpus of text to predict the next word in a sentence by looking at its previous context.

Fine-Tuning GPT-3

The researchers fine-tuned OpenAI’s GPT-3 language model using the work of philosopher Daniel C. Dennett. It was concluded that the model could produce responses that closely mirror human philosophers’ answers. The team wanted to evaluate their fine-tuned model by asking it questions and examining if its answers were something the actual philosopher could have given.

Testing the Model

The researchers collected four responses for each question without cherry-picking, that is, without necessarily choosing the best results, by asking Dennett ten philosophical questions and then posing the same questions to their language model. They then asked 425 human users if they could tell the difference between responses to philosophical queries given by Dennett and those created by the machine. It was astounding to discover that expert philosophers and readers of philosophy blogs could correctly identify Dennett’s responses roughly 50% of the time. In contrast, average participants with little to no philosophical background did so only 20% of the time. These findings imply that a GPT-3 model that has been fine-tuned can be surprisingly close to speaking in the voice of a certain philosopher.

Future Plans

Even though the language model delivered impressive results, there is still an opportunity for improvement. The team intends to develop their model further and apply it to more real-world scenarios in the future. Also, they are investigating the potential for making it into a tool that would be very useful to philosophers and historians.


In conclusion, the researchers have demonstrated that large language models can be taught to write philosophical texts that are virtually indistinguishable from those written by actual philosophers. This is an incredible development in the field of AI and has the potential to revolutionize the way we interact with AI models in the future.

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