Measuring a machine's understanding of human language often involves
assessing its reasoning skills, i.e. logical process of deriving answers to
questions. While recent language models have shown remarkable proficiency in
text based tasks, their efficacy in complex reasoning problems involving
heterogeneous information such as text, tables, and numbers remain uncertain.
Addressing this gap, FinQA introduced a numerical reasoning dataset for
financial documents and simultaneously proposed a program generation approach .
Our investigation reveals that half of the errors (48%) stem from incorrect
operations being generated. To address this issue, we propose a novel approach
to tackle numerical reasoning problems using case based reasoning (CBR), an
artificial intelligence paradigm that provides problem solving guidance by
offering similar cases (i.e. similar questions and corresponding logical
programs). Our model retrieves relevant cases to address a given question, and
then generates an answer based on the retrieved cases and contextual
information. Through experiments on the FinQA dataset, we demonstrate
competitive performance of our approach and additionally show that by expanding
case repository, we can help solving complex multi step programs which FinQA
showed weakness of.

通过引入案例推理和扩展案例库，我们提出了一种用于解决金融文档中的数字推理问题的新方法，该方法在 FinQA 数据集上表现出竞争性能，并显示出了复杂多步骤程序的解决能力。

基于案例推理的金融问答解决方法

Case-Based Reasoning Approach for Solving Financial Question Answering

Recently, it has been proposed that fruitful synergies may exist between Deep
Learning (DL) and Case Based Reasoning (CBR); that there are insights to be
gained by applying CBR ideas to problems in DL (what could be called DeepCBR).
In this paper, we report on a program of research that applies CBR solutions to
the problem of Explainable AI (XAI) in the DL. We describe a series of
twin-systems pairings of opaque DL models with transparent CBR models that
allow the latter to explain the former using factual, counterfactual and
semi-factual explanation strategies. This twinning shows that functional
abstractions of DL (e.g., feature weights, feature importance and decision
boundaries) can be used to drive these explanatory solutions. We also raise the
prospect that this research also applies to the problem of Data Augmentation in
DL, underscoring the fecundity of these DeepCBR ideas.

本文介绍运用深度情景案例推理与处理 (Case Based Reasoning) 方法让深度学习模型变得透明可解释，进一步探讨了深度学习在数据扩充和可解释 AI 方面的应用。