4 papers · 1 filter
LLM Reasoning for Machine Translation: Synthetic Data Generation over Thinking Tokens
Armel Zebaze, Rachel Bawden, Benoît Sagot
Large reasoning models (LRMs) have led to new possibilities in terms of problem-solving, through the devising of a natural language thought process prior to answering a query. Whil…
TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation
Armel Zebaze, Benoît Sagot, Rachel Bawden
LLMs have been shown to perform well in machine translation (MT) with the use of in-context learning (ICL), rivaling supervised models when translating into high-resource languages…
Compositional Translation: A Novel LLM-based Approach for Low-resource Machine Translation
Armel Zebaze, Benoît Sagot, Rachel Bawden
The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural…
Tree of Problems: Improving structured problem solving with compositionality
Armel Zebaze, Benoît Sagot, Rachel Bawden
Large Language Models (LLMs) have demonstrated remarkable performance across multiple tasks through in-context learning. For complex reasoning tasks that require step-by-step think…