Creating a large language model of a philosopher
Can large language models be trained to produce philosophical texts that are difficult to distinguish from texts produced by human philosophers? To address this question, we fine-tuned OpenAI’s GPT-3 with the works of philosopher Daniel C. Dennett as additional training data. To explore the Dennett model, we asked the real Dennett ten philosophical questions and then posed the same questions to the language model, collecting four responses for each question without cherry-picking. We recruited 425 participants to distinguish Dennett’s answer from the four machine-generated answers. Experts on Dennett’s work (N = 25) succeeded 51% of the time, above the chance rate of 20% but short of our hypothesized rate of 80% correct. For two of the ten questions, the language model produced at least one answer that experts selected more frequently than Dennett’s own answer. Philosophy blog readers (N = 302) performed similarly to the experts, while ordinary research participants (N = 98) were near chance distinguishing GPT-3’s responses from those of an “actual human philosopher”.
We built a chatbot that can take human instructions and perform tasks on browser to validate the concept of LLM in action. AGI (artificial general intelligence) with internet can benefit human with real-time information, higher productivity, and wider source of truth, and will potentially be the next trend in AIGC. The chatbot is built using […]
You can use Artificial Intelligence directly within Reflect to drastically improve your note-taking and writing. Think of it as a digital assistant you can call on at any time, and ask anything you think of.
Latitude’s AI SQL Assistant uses Open AI’s GPT-3 Models to suggest code and entire SQL functions in real-time, based on your prompts and right from your data project canvas. Latitude was born with a clear focus: help companies extract value from data by improving the way you interact with it. Our team has worked (and struggled) […]
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