# OpenQA场景

This approach scales well with the number of retrieved passages, as the performance keeps improving when retrieving up to one hundred passages.

# FiD模型

FiD的想法简单直接，将检索回来的每个passage都与question通过encoder分别编码，然后concat在一起输入decoder生成最终的回复。顾名思义，叫做Fusion-in-Decoder

FiD模型的效果还出奇的好：

While conceptually simple, this method sets new state-of-the-art results on the TriviaQA and NaturalQuestions benchmarks.

We believe that this is evidence that generative mod els are good at combining evidence from multiple passages, compared to extractive ones.

# 实验结果

FiD在三个数据集：NaturalQuestions, TriviaQA, SQuAD 上的表现都非常好。

In particular, we observe that increasing the number of passages from 10 to 100 leads to 6% improvement on TriviaQA and 3.5% improvement on NaturalQuestions.

FiD的方案简单直接，但随着passage数目的不断增多，经concat之后decoder的输入会变得很长，训练起来的成本也随之增高不少。从 FiD github repo 的一段说明可见一斑：

Training these models with 100 passages is memory intensive. To alleviate this issue we use checkpointing with the --use_checkpoint option. ... The large readers have been trained on 64 GPUs ...