# Creating a Large Language Model of a Philosopher

arXiv: https://arxiv.org/abs/2302.01339 · HTML: https://mlsanity.com/paper/2302.01339/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 71 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- [Emergent Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2212.09196/) (2022)
- [Why We Need New Evaluation Metrics for NLG](https://mlsanity.com/paper/1707.06875/) (2017)
- [Implicit Representations of Meaning in Neural Language Models](https://mlsanity.com/paper/2106.00737/) (2021)
- [Talking About Large Language Models](https://mlsanity.com/paper/2212.03551/) (2022)
- [oLMpics -- On what Language Model Pre-training Captures](https://mlsanity.com/paper/1912.13283/) (2019)
- [The Debate Over Understanding in AI's Large Language Models](https://mlsanity.com/paper/2210.13966/) (2022)
- [Language Models as Agent Models](https://mlsanity.com/paper/2212.01681/) (2022)
- [DeepBach: a Steerable Model for Bach Chorales Generation](https://mlsanity.com/paper/1612.01010/) (2016)
- [Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text](https://mlsanity.com/paper/2107.01294/) (2021)
- [RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text](https://mlsanity.com/paper/2010.03070/) (2020)
- [What Do NLP Researchers Believe? Results of the NLP Community Metasurvey](https://mlsanity.com/paper/2208.12852/) (2022)

## Built on by (most cited first, 21 of 21)

- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [Securing Large Language Models: Addressing Bias, Misinformation, and Prompt Attacks](https://mlsanity.com/paper/2409.08087/) (2024)
- [Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory](https://mlsanity.com/paper/2406.14373/) (2024)
- [AI Text-to-Behavior: A Study In Steerability](https://mlsanity.com/paper/2308.07326/) (2023)
- [ProgressGym: Alignment with a Millennium of Moral Progress](https://mlsanity.com/paper/2406.20087/) (2024)
- [On pitfalls (and advantages) of sophisticated large language models](https://mlsanity.com/paper/2303.17511/) (2023)
- [Transforming Agency. On the mode of existence of Large Language Models](https://mlsanity.com/paper/2407.10735/) (2024)
- [Language Models as Critical Thinking Tools: A Case Study of Philosophers](https://mlsanity.com/paper/2404.04516/) (2024)
- [Philosophy of Cognitive Science in the Age of Deep Learning](https://mlsanity.com/paper/2405.04048/) (2024)
- [Palatable Conceptions of Disembodied Being](https://mlsanity.com/paper/2503.16348/) (2025)
- [Who's the Best Detective? LLMs vs. MLs in Detecting Incoherent Fourth Grade Math Answers](https://mlsanity.com/paper/2304.11257/) (2023)
- [Potential Benefits of Employing Large Language Models in Research in Moral Education and Development](https://mlsanity.com/paper/2306.13805/) (2023)
- [Carbon and Silicon, Coexist or Compete? A Survey on Human-AI Interactions in Agent-based Modeling and Simulation](https://mlsanity.com/paper/2502.18145/) (2025)
- [AI Agents Alone Are Not (Yet) Sufficient for Social Simulation](https://mlsanity.com/paper/2603.00113/) (2026)
- [Can AI and humans genuinely communicate?](https://mlsanity.com/paper/2402.09494/) (2024)
- [Integrating LLM and Diffusion-Based Agents for Social Simulation](https://mlsanity.com/paper/2510.16366/) (2025)
- [Artificial intelligence and the limits of the humanities](https://mlsanity.com/paper/2310.19425/) (2023)
- [Can a Hallucinating Model help in Reducing Human "Hallucination"?](https://mlsanity.com/paper/2405.00843/) (2024)
- [AI and Consciousness](https://mlsanity.com/paper/2510.09858/) (2025)
- [The Making of Digital Ghosts: Designing Ethical AI Afterlives](https://mlsanity.com/paper/2511.20094/) (2025)

## Abstract (arXiv)

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".