Aligning Neural Machine Translation Models: Human Feedback in Training
  and Inference
Miguel Moura Ramos Patrick Fernandes António Farinhas André F. T. Martins
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 generate. A core ingredient in RLHF's success in aligning
and improving large language models (LLMs) is its reward model, trained using
human feedback on model outputs. In machine translation (MT), where metrics
trained from human annotations can readily be used as reward models, recent
methods using minimum Bayes risk decoding and reranking have succeeded in
improving the final quality of translation. In this study, we comprehensively
explore and compare techniques for integrating quality metrics as reward models
into the MT pipeline. This includes using the reward model for data filtering,
during the training phase through RL, and at inference time by employing
reranking techniques, and we assess the effects of combining these in a unified
approach. Our experimental results, conducted across multiple translation
tasks, underscore the crucial role of effective data filtering, based on
estimated quality, in harnessing the full potential of RL in enhancing MT
quality. Furthermore, our findings demonstrate the effectiveness of combining
RL training with reranking techniques, showcasing substantial improvements in
translation quality.