RL with chrF reward trains LLMs to better utilize in-context linguistic knowledge for zero-shot translation of unseen languages, outperforming ICL and SFT.
arXiv preprint arXiv:2302.07856 , year=
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Releases a 457-sentence Komi-Yazva--Russian parallel corpus and shows that retrieval-based few-shot prompting improves LLM translation over zero-shot in this low-resource setting, with performance varying by model and metric.
VIDA provides 2,500 visually-dependent ambiguous translation examples and span-level disambiguation metrics; CoT-SFT on LVLMs improves out-of-distribution performance over standard SFT.
Random forests on string similarity features outperform LLMs for German dialect lexicon induction and boost dialect information retrieval by up to 50% in recall.
citing papers explorer
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Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation
RL with chrF reward trains LLMs to better utilize in-context linguistic knowledge for zero-shot translation of unseen languages, outperforming ICL and SFT.
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A Komi-Yazva--Russian Parallel Corpus and Evaluation Protocol for Zero- and Few-Shot LLM Translation
Releases a 457-sentence Komi-Yazva--Russian parallel corpus and shows that retrieval-based few-shot prompting improves LLM translation over zero-shot in this low-resource setting, with performance varying by model and metric.
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VIDA: A dataset for Visually Dependent Ambiguity in Multimodal Machine Translation
VIDA provides 2,500 visually-dependent ambiguous translation examples and span-level disambiguation metrics; CoT-SFT on LVLMs improves out-of-distribution performance over standard SFT.
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Resource-Lean Lexicon Induction for German Dialects
Random forests on string similarity features outperform LLMs for German dialect lexicon induction and boost dialect information retrieval by up to 50% in recall.