Multi-hop Few-shot Open Rich Table Question Answering: Leveraging Large Language Models and Retrieval Techniques for Accurate Answers

מושגי ליבה

MFORT-QA leverages few-shot learning, chain-of-thought prompting, and retrieval-augmented generation to accurately answer complex questions by extracting relevant information from tables and associated hyperlinked contexts.

תקציר

The paper introduces MFORT-QA, a novel approach to multi-hop few-shot open rich table question answering. The key highlights are:

Few-Shot Learning (FSL): MFORT-QA uses FSL to prompt large language models (LLMs) like ChatGPT with relevant table-question-answer examples, enabling the LLM to generate accurate answers from the retrieved tables.

Chain-of-Thought (CoT) Prompting: For complex questions that the LLM struggles to answer directly, MFORT-QA employs CoT to break down the original question into simpler sub-questions with reasoning thoughts.

Retrieval-Augmented Generation (RAG): MFORT-QA uses RAG to retrieve additional relevant tables and hyperlinked passages to supplement the initial prompt, further enhancing the LLM's ability to generate accurate answers.

The experiments on the OTT-QA dataset demonstrate that MFORT-QA significantly outperforms traditional extractive table and text QA models, as well as LLMs with zero-shot learning, in terms of F1 score, precision, recall, and exact match.

סטטיסטיקה

"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."
"Recent advancements in Large Language Models (LLMs) have opened up new possibilities for extracting information from tabular data using prompts."

ציטוטים

"To address this challenge, the approach of Table Question Answering (QA) has been developed to extract the relevant information."
"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)."
"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."

תובנות מפתח מזוקקות מ:

MFORT-QA

by Che Guan,Men... ב- arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19116.pdf

שאלות מעמיקות

How can MFORT-QA be extended to handle more diverse types of tabular data beyond Wikipedia tables?

To extend MFORT-QA to handle more diverse types of tabular data beyond Wikipedia tables, several strategies can be implemented:

Dataset Expansion: Incorporating additional datasets from various domains such as healthcare, finance, or legal fields can provide a more diverse range of tabular data for training and testing. This expansion can help the model generalize better to different types of tables.

Domain-Specific Fine-Tuning: Fine-tuning MFORT-QA on domain-specific data can enhance its ability to understand and extract information from tables in those particular domains. This targeted fine-tuning can improve performance on specialized tabular data.

Table Representation Learning: Implementing techniques for learning more robust representations of tabular data can help MFORT-QA adapt to different table structures and formats. Methods like graph neural networks or attention mechanisms tailored for tables can be explored.

Hyperlink Context Handling: Enhancing the model's capability to extract information from hyperlinked passages within tables is crucial for handling diverse tabular data. Improving the retrieval and processing of hyperlinked content can make MFORT-QA more versatile.

Multi-Modal Integration: Integrating multi-modal information, such as images or charts associated with tables, can enrich the data input for MFORT-QA. This integration can enable the model to extract information from a wider range of sources within tabular data.

By incorporating these strategies, MFORT-QA can be extended to handle a broader spectrum of tabular data types beyond Wikipedia tables, enhancing its applicability across various domains and use cases.

What are the potential limitations of the CoT and RAG approaches, and how can they be addressed to further improve the performance of MFORT-QA?

Limitations of CoT and RAG Approaches:

Complexity: CoT may introduce complexity in breaking down questions into sub-queries, leading to potential errors in the reasoning chain. RAG's retrieval process may also introduce noise from irrelevant context, impacting answer generation.

Scalability: Handling multi-hop reasoning in CoT for extremely complex questions can be computationally intensive and time-consuming. RAG's retrieval process may struggle with scalability when dealing with large datasets.

Dependency on Retrieval Quality: RAG heavily relies on the quality of retrieved context, which can be challenging when dealing with noisy or irrelevant information. Inaccurate retrieval can lead to incorrect answers.

Addressing Limitations:

Improved Reasoning Strategies: Enhancing the reasoning strategies in CoT by incorporating more advanced logic and context-aware reasoning mechanisms can mitigate errors in the reasoning chain.

Efficient Retrieval Mechanisms: Implementing more efficient retrieval mechanisms in RAG, such as advanced filtering techniques or relevance scoring, can improve the quality of retrieved context and reduce noise.

Model Optimization: Fine-tuning the models used in CoT and RAG to better handle scalability issues can improve performance. Optimizing the architecture and parameters can enhance efficiency without compromising accuracy.

Noise Reduction Techniques: Implementing noise reduction techniques, such as context filtering or context summarization, can help eliminate irrelevant information retrieved by RAG, improving the model's ability to generate accurate answers.

By addressing these limitations through advanced strategies and optimizations, the performance of CoT and RAG in MFORT-QA can be enhanced, leading to more accurate and reliable question-answering capabilities.

Given the rapid advancements in large language models, how might MFORT-QA evolve to leverage emerging models and techniques, such as GPT-4, to enhance its capabilities in the future?

Model Integration: MFORT-QA can integrate GPT-4 or other advanced language models to benefit from their enhanced capabilities in understanding complex language structures and generating more accurate answers. Leveraging the advancements in GPT-4 can improve the overall performance of MFORT-QA.

Fine-Tuning Strategies: Implementing fine-tuning strategies specific to GPT-4 can optimize MFORT-QA for compatibility with the latest model architecture. Fine-tuning on domain-specific data can further enhance the model's performance.

Multi-Modal Fusion: Exploring multi-modal fusion techniques to combine text and tabular data with other modalities like images or graphs can enrich the input for MFORT-QA. This integration can leverage GPT-4's multimodal capabilities for more comprehensive question-answering.

Efficient Computation: Optimizing the computational efficiency of MFORT-QA to handle the increased complexity of models like GPT-4 is crucial. Implementing parallel processing or distributed computing can enhance performance without compromising speed.

Continuous Learning: Incorporating continual learning techniques to adapt MFORT-QA to evolving language models like GPT-4 can ensure the model stays up-to-date with the latest advancements. Regular updates and retraining can maintain the model's relevance and accuracy.

By evolving MFORT-QA to leverage emerging models like GPT-4 through these strategies, the system can stay at the forefront of advancements in large language models, enhancing its capabilities and performance in handling complex table-based question-answering tasks.