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

• URL: http://arxiv.org/abs/2403.19116v1
• Date: Thu, 28 Mar 2024 03:14:18 GMT
• Title: MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering
• Authors: Che Guan, Mengyu Huang, Peng Zhang,
• Abstract summary: 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. To address this challenge, the approach of Table Question Answering (QA) has been developed to extract the relevant information. Recent advancements in Large Language Models (LLMs) have opened up new possibilities for extracting information from tabular data using prompts.
• Score: 3.1651118728570635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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