# Explicit Learning and the LLM in Machine Translation

arXiv: https://arxiv.org/abs/2503.09454 · HTML: https://mlsanity.com/paper/2503.09454/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2025.

## Built on by (most cited first, 6 of 6)

- [ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline](https://mlsanity.com/paper/2508.06094/) (2025)
- [LLM Reasoning for Machine Translation: Synthetic Data Generation over Thinking Tokens](https://mlsanity.com/paper/2510.11919/) (2025)
- [Speaking of Language: Reflections on Metalanguage Research in NLP](https://mlsanity.com/paper/2604.02645/) (2026)
- [RespondeoQA: a Benchmark for Bilingual Latin-English Question Answering](https://mlsanity.com/paper/2604.20738/) (2026)
- [Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation](https://mlsanity.com/paper/2606.06428/) (2026)
- [LLM Agents as Computational Typologists](https://mlsanity.com/paper/2609.07791/) (2026)

## Abstract (arXiv)

This study explores an LLM's ability to learn new languages using explanations found in a grammar book, a process we term "explicit learning." To rigorously assess this ability, we design controlled translation experiments between English and constructed languages generated, through specific cryptographic means, from Latin or French. Contrary to previous studies, our results demonstrate that LLMs do possess a measurable capacity for explicit learning. This ability, however, diminishes as the complexity of the linguistic phenomena to be learned increases. Supervised fine-tuning on ad hoc 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, benefiting low-resource languages typically described in grammar books but lacking extensive corpora.