Retrieval-Augmented Generation (RAG), by incorporating external knowledge
with parametric memory of language models, has become the state-of-the-art
architecture for open-domain QA tasks. However, common knowledge bases are
inherently constrained by limited coverage and noisy information, making
retrieval-based approaches inadequate to answer implicit reasoning questions.
In this paper, we propose an Induction-Augmented Generation (IAG) framework
that utilizes inductive knowledge along with the retrieved documents for
implicit reasoning. We leverage large language models (LLMs) for deriving such
knowledge via a novel prompting method based on inductive reasoning patterns.
On top of this, we implement two versions of IAG named IAG-GPT and IAG-Student,
respectively. IAG-GPT directly utilizes the knowledge generated by GPT-3 for
answer prediction, while IAG-Student gets rid of dependencies on GPT service at
inference time by incorporating a student inductor model. The inductor is
firstly trained via knowledge distillation and further optimized by
back-propagating the generator feedback via differentiable beam scores.
Experimental results show that IAG outperforms RAG baselines as well as ChatGPT
on two Open-Domain QA tasks. Notably, our best models have won the first place
in the official leaderboards of CSQA2.0 (since Nov 1, 2022) and StrategyQA
(since Jan 8, 2023).

基于检索增强生成（RAG）的框架在开放域 QA 任务中成为最先进的架构，本文提出了一种利用归纳知识和检索文档进行隐性推理的归纳增强生成（IAG）框架，并展示了 IAG 在开放域 QA 任务中的性能优越性。

IAG: 增强归纳生成框架用于回答推理问题

IAG: Induction-Augmented Generation Framework for Answering Reasoning  Questions

BERT model has been successfully applied to open-domain QA tasks. However,
previous work trains BERT by viewing passages corresponding to the same
question as independent training instances, which may cause incomparable scores
for answers from different passages. To tackle this issue, we propose a
multi-passage BERT model to globally normalize answer scores across all
passages of the same question, and this change enables our QA model find better
answers by utilizing more passages. In addition, we find that splitting
articles into passages with the length of 100 words by sliding window improves
performance by 4%. By leveraging a passage ranker to select high-quality
passages, multi-passage BERT gains additional 2%. Experiments on four standard
benchmarks showed that our multi-passage BERT outperforms all state-of-the-art
models on all benchmarks. In particular, on the OpenSQuAD dataset, our model
gains 21.4% EM and 21.5% $F_1$ over all non-BERT models, and 5.8% EM and 6.5%
$F_1$ over BERT-based models.

本文提出了一种多样化的 BERT 模型，通过使用多个片段并全局规范化答案分数，并得到统一的分数，性能提高了 4％。通过使用段落评分器选择高质量的段落，多通道 BERT 获得了额外的 2％。实验结果表明，我们的方案在所有基准测试上均优于现有模型，特别是在 OpenSQuAD 数据集上，我们的模型比所有非 BERT 模型和基于 BERT 的模型都表现出色。