Seth Aycock — accepted AI-conference papers

2025

Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?

Seth Aycock, David Stap, Di Wu, Christof Monz, Khalil Sima'an

ICLR 2025spotlight

Extremely low-resource (XLR) languages lack substantial corpora for training NLP models, motivating the use of all available resources such as dictionaries and grammar books. Machine Translation from One Book (Tanzer et al., 2024) suggests that prompting long-context LLMs with one grammar book enabl…

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2025

Please Translate Again: Two Simple Experiments on Whether Human-Like Reasoning Helps Translation

Di Wu, Seth Aycock, Christof Monz

EMNLP 2025

Large Language Models (LLMs) demonstrate strong reasoning capabilities for many tasks, often by explicitly decomposing the task via Chain-of-Thought (CoT) reasoning. Recent work on LLM-based translation designs hand-crafted prompts to decompose translation, or trains models to incorporate intermedia

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