In this paper, we propose a methodology to align a medium-sized GPT model,
originally trained in English for an open domain, to a small closed domain in
Spanish. The application for which the model is finely tuned is the question
answering task. To achieve this we also needed to train and implement another
neural network (which we called the reward model) that could score and
determine whether an answer is appropriate for a given question. This component
served to improve the decoding and generation of the answers of the system.
Numerical metrics such as BLEU and perplexity were used to evaluate the model,
and human judgment was also used to compare the decoding technique with others.
Finally, the results favored the proposed method, and it was determined that it
is feasible to use a reward model to align the generation of responses.

本文提出一种将原本用于英语开放域的中型 GPT 模型对齐到西班牙语的小型闭域的方法，并使用神经网络训练和实现了 “奖励模型”，来提高系统答案的解码和生成，结果表明这种方法是可行的。