Explicit Learning and the LLM in Machine Translation

This study explores the capacity of large language models (LLMs) for explicit learning, a process involving the assimilation of metalinguistic explanations to carry out language tasks. Using constructed languages generated by cryptographic means as controlled test environments, we designed experiments to assess an LLM's ability to explicitly learn and apply grammar rules. Our results demonstrate that while LLMs possess a measurable capacity for explicit learning, this ability diminishes as the complexity of the linguistic phenomena at hand increases. Supervised fine-tuning on chains of thought significantly enhances LLM performance but struggles to generalize to typologically novel or more complex linguistic features. These findings point to the need for more diverse training sets and alternative fine-tuning strategies to further improve explicit learning by LLMs.