When the Pure Reasoner Meets the Impossible Object: Analytic vs. Synthetic Fine-Tuning and the Suppression of Genesis in Language Models

1. 1Investigate the concept of impossible objects and their definition by mutually exclusive predicates
2. 2Apply the Kantian distinction between analytic and synthetic judgments to fine-tuning LLMs
3. 3Subject LLMs to distinct training regimes, such as analytic and synthetic adapters
4. 4Analyze the ontological consequences of fine-tuning on impossible objects

AI researchers and engineers working with LLMs can benefit from understanding the implications of fine-tuning on impossible objects, while product managers and ML engineers can apply these findings to improve model performance

💡 Fine-tuning LLMs on impossible objects can lead to a suppression of genesis, highlighting the importance of understanding the ontological implications of model training

💡 Fine-tuning LLMs on impossible objects reveals insights into analytic & synthetic judgments