Aligning Neural Machine Translation Models: Human Feedback in Training and Inference

Understand

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.