This AI research presents an extensive language model that can answer philosophical questions in the voice of a specific philosopher

As artificial intelligence has exploded in recent years, models can now outperform humans at a wide range of tasks, from playing games like chess to doing practical tasks like predicting protein structures and performing matrix multiplication calculations. . Large language models have benefited significantly from recent technological improvements that have led to the creation of sophisticated information and dialog systems. One of the best examples of how language models work amazingly well when composing documents, conversing with humans, and answering questions is ChatGPT.

Another recent field of research that has intrigued scholars is whether AI can also write innovative and intelligent philosophical essays. It is now believed that the expert-level professional philosophy requires a form of competence and knowledge that existing AI models still require. It would be fascinating to find out if great language models could be taught to write philosophical texts that were virtually indistinguishable from those written by real philosophers. To address this problem statement, researchers at the University of California-Riverside, École Normale Supérieure (ECN) in Paris, and Ludwig-Maximilians-Universität München created a large language model that can answer philosophical queries in much the same way as of a particular philosopher. The group refined 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 the responses of human philosophers.

GPT-3, or the Third Generation Generative Pretrained Transformer, is an autoregressive language model that uses deep learning to generate texts. The basis of the model lies in the use of sophisticated and robust statistical algorithms in the input text indicator to predict the next word in a sentence. To do this, the language model analyzes a massive corpus of text to predict the next word in a sentence by looking at its previous context. The researchers adjusted the GPT-3 model based on Dennett’s earlier writing to ensure that it gave more weight to the philosopher’s typical word-use patterns in predicting the next word in a sentence than other word patterns.

The researchers wanted to test their perfected model by asking it questions and examining whether its answers were something the actual philosopher might have given. The researchers collected four responses to each question without choosing, that is, without necessarily choosing the best results, by asking Dennett ten philosophical questions and then posing the same questions to his language model. They then asked 425 human users if they could tell the difference between answers to philosophical questions given by Dennett and those created by the machine. It was amazing to find that expert philosophers and readers of philosophy blogs could correctly identify Dennett’s answers about 50% of the time. In contrast, the average participants with little or no philosophical training did so only 20% of the time. These findings imply that a GPT-3 model that has been fitted may come surprisingly close to speaking in the voice of a certain philosopher.

Despite the fact that the language model delivered impressive results, there is still an opportunity for improvement. The team intends to further develop their model and apply it to more real-world scenarios in the future. Furthermore, they are investigating the potential to turn it into a tool that would be of great use to philosophers and historians.

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