Papers by Seth Aycock
Topic-guided Example Selection for Domain Adaptation in LLM-based Machine Translation (2024.eacl-srw)
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| Challenge: | Current machine translation (MT) systems perform well in domains on which they were trained, but adaptation to unseen domains remains a challenge. |
| Approach: | They propose to use large language models to adapt to unseen domains by in-context example selection. |
| Outcome: | The proposed method outperforms baselines on multilingual out-of-domain tests, though it does not match performance with strong baselines for the in-language setting. |
How Important is ‘Perfect’ English for Machine Translation Prompts? (2026.findings-eacl)
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Patrícia Schmidtová, Niyati Bafna, Seth Aycock, Gianluca Vico, Wiktor Kamzela, Kathy Hämmerl, Vilém Zouhar
| Challenge: | Large language models (LLMs) are largely trained on and respond best to English prompts, but are also sensitive to errors in user prompts. |
| Approach: | They propose to model a range of error types exhibited by second language English speakers and quantify their impact on LLM performance. |
| Outcome: | The proposed model is brittle to natural spelling errors but not to errors at the phrasal level, but the variance in quality caused by these errors is lower than the variance over the initial prompt choice. |
Please Translate Again: Two Simple Experiments on Whether Human-Like Reasoning Helps Translation (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) demonstrate strong reasoning capabilities for many tasks, often by explicitly decomposing the task via Chain-of-Thought (CoT) reasoning. |
| Approach: | They propose to use a multi-step prompt to decompose translation and train models to incorporate intermediate steps. |
| Outcome: | The proposed model achieves state-of-the-art on WMT24 data and shows that prompting LLMs to “translate again” and self-refine yields even better results than human-like step-by-step prompting. |