MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering

📄 arXiv: 2403.19116v1 📥 PDF

作者: Che Guan, Mengyu Huang, Peng Zhang

分类: cs.CL, cs.AI

发布日期: 2024-03-28

备注: 8 pages

💡 一句话要点

提出MFORT-QA以解决复杂表格问答问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 表格问答 少样本学习 链式思维 信息检索 大语言模型 推理增强 复杂问题处理

📋 核心要点

  1. 现有的表格问答方法在处理复杂问题时，往往无法确保提取到准确的答案，存在一定的局限性。
  2. MFORT-QA方法通过少样本学习和链式思维提示，将复杂问题分解为多个简单问题，从而提高了答案的准确性。
  3. 实验结果表明，MFORT-QA在OTT-QA数据集上显著提高了提取式表格问答方法的准确性，展示了其有效性。

📝 摘要（中文）

在当今快节奏的行业中，专业人士面临着从大量文档中提取关键信息的挑战，这些信息常常隐藏在表格及其嵌套超链接中。为了解决这一问题，表格问答（QA）方法应运而生。然而，传统的表格QA训练任务可能无法确保提取准确答案。本文提出了多跳少样本开放丰富表格问答（MFORT-QA）方法，分为两个主要步骤：首先，通过少样本学习（FSL）检索相关表格及其超链接上下文；其次，利用链式思维（CoT）提示将复杂问题分解为多跳的推理链。通过检索增强生成（RAG）进一步提高了答案的准确性，实验证明该方法显著提升了提取式表格QA的准确性。

🔬 方法详解

问题定义：本文旨在解决复杂表格问答中的信息提取问题，现有方法在处理复杂问题时常常无法确保答案的准确性。

核心思路：MFORT-QA通过少样本学习（FSL）和链式思维（CoT）提示，将复杂问题分解为多个简单问题，利用大语言模型（LLM）进行推理，从而提高答案的准确性。

技术框架：该方法包括两个主要步骤：第一步是通过FSL检索相关表格及其上下文，构建少样本提示；第二步是利用CoT提示将复杂问题分解为多跳推理链，并通过RAG增强检索相关上下文。

关键创新：MFORT-QA的创新在于结合了FSL和CoT提示，能够有效处理复杂问题，并通过RAG增强信息检索，显著提高了答案的准确性。

关键设计：在参数设置上，采用了适合FSL的少样本提示构建策略，损失函数设计上注重推理链的连贯性，确保了模型在多跳推理中的有效性。

📊 实验亮点

实验结果显示，MFORT-QA在OTT-QA数据集上的表现显著优于传统提取式表格问答方法，准确率提升幅度达到XX%，验证了其在复杂问答场景中的有效性。

🎯 应用场景

MFORT-QA方法在信息提取、智能问答系统和数据分析等领域具有广泛的应用潜力。其能够帮助专业人士快速从复杂数据中提取关键信息，提高工作效率，未来可在更多行业中推广应用。

📄 摘要（原文）

In today's fast-paced industry, professionals face the challenge of summarizing a large number of documents and extracting vital information from them on a daily basis. These metrics are frequently hidden away in tables and/or their nested hyperlinks. To address this challenge, the approach of Table Question Answering (QA) has been developed to extract the relevant information. However, traditional Table QA training tasks that provide a table and an answer(s) from a gold cell coordinate(s) for a question may not always ensure extracting the accurate answer(s). Recent advancements in Large Language Models (LLMs) have opened up new possibilities for extracting information from tabular data using prompts. In this paper, we introduce the Multi-hop Few-shot Open Rich Table QA (MFORT-QA) approach, which consists of two major steps. The first step involves Few-Shot Learning (FSL), where relevant tables and associated contexts of hyperlinks are retrieved based on a given question. The retrieved content is then used to construct few-shot prompts as inputs to an LLM, such as ChatGPT. To tackle the challenge of answering complex questions, the second step leverages Chain-of-thought (CoT) prompting to decompose the complex question into a sequential chain of questions and reasoning thoughts in a multi-hop manner. Retrieval-Augmented Generation (RAG) enhances this process by retrieving relevant tables and contexts of hyperlinks that are relevant to the resulting reasoning thoughts and questions. These additional contexts are then used to supplement the prompt used in the first step, resulting in more accurate answers from an LLM. Empirical results from OTT-QA demonstrate that our abstractive QA approach significantly improves the accuracy of extractive Table QA methods.