Some of the latest AI research projects address a fundamental issue in the performance of large auto-regressive language models (LLMs) such as GPT-3 and GPT-4. This issue, referred to as the “Reversal Curse,” pertains to the model’s ability to generalize information learned during training. Specifically, when these models are trained on sentences following the format “A is B,” they often struggle to automatically reverse this information to answer questions in the format “B is A.” This limitation points to a deficiency in logical deduction and generalization, which are critical for these models to understand and respond accurately to various types of queries.At present, there is no established method or framework to completely mitigate the Reversal Curse in auto-regressive LLMs. The research aims to identify and characterize this limitation, shedding light on the challenges it poses to language models. While there have been studies focusing on the influence of training data on LLMs and how they store and recall facts, addressing the Reversal Curse remains an ongoing challenge.
Aristotle, thou shouldst be living at this hour.
Bear this in mind the next time you hear someone getting carried away by the possibilities of generative AI, or being dismissive of the drawbacks.