2 citations · 5 across the 17 of their papers we have counts for
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cs.CL2023★ 1 cited
Aligning Neural Machine Translation Models: Human Feedback in Training and Inference
Miguel Moura Ramos, Patrick Fernandes, António Farinhas +1
Reinforcement learning from human feedback (RLHF) is a recent technique to improve the quality of the text generated by a language model, making it closer to what humans would gene…
cs.CL2023
An Empirical Study of Translation Hypothesis Ensembling with Large Language Models
António Farinhas, José G. C. de Souza, André F. T. Martins
Large language models (LLMs) are becoming a one-fits-many solution, but they sometimes hallucinate or produce unreliable output. In this paper, we investigate how hypothesis ensemb…