Papers by José de Souza
An Empirical Study of Translation Hypothesis Ensembling with Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) are becoming a one-fits-many solution, but they sometimes hallucinate or produce unreliable output. |
| Approach: | They propose to use several LLMs to ensemble translation hypotheses . they use instruction tuning, quality-based reranking, and minimum Bayes risk (MBR) decoding to improve translation quality. |
| Outcome: | The proposed method improves translation quality and instruction tuning improves the quality of the output. |
Steering Large Language Models for Machine Translation with Finetuning and In-Context Learning (2023.findings-emnlp)
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Duarte Alves, Nuno Guerreiro, João Alves, José Pombal, Ricardo Rei, José de Souza, Pierre Colombo, Andre Martins
| Challenge: | Large language models (LLMs) are a promising avenue for machine translation (MT) however, their effectiveness depends on the choice of few-shot examples and they often require extra post-processing due to overgeneration. |
| Approach: | They propose a method that incorporates few-shot examples during finetuning to improve performance on MT tasks. |
| Outcome: | The proposed method outperforms few-shot prompting while eliminating the need for in-context examples. |