Enhancing Large Language Model-based Question Answering with Domain Hybrid Data: Evaluating the Impact of Table-to-Text Generation Methods

Core Concepts

Different table-to-text generation methods significantly impact the performance of LLM-based question answering systems when leveraging domain hybrid data, with the LLM-based and TPLM-based methods exhibiting superior performance in the DSFT paradigm, and the LLM-based and Markdown methods showing excellence in the RAG paradigm.

Abstract

This paper explores the impact of different table-to-text generation methods on enhancing LLM-based question answering (QA) systems with domain hybrid data. The authors first innovatively integrate table-to-text generation into the framework of improving LLM-based QA systems using domain-specific data. They then conduct extensive experiments on two types of QA systems (DSFT and RAG) with four representative table-to-text methods: Markdown format, Template serialization, TPLM-based, and LLM-based.
The key findings are:

Table-to-text methods significantly impact the performance of QA systems, with relative score differences ranging from 2.8% to 9.0% in human evaluation and 4.8% to 16% in GPT-4 evaluation.

In the DSFT paradigm, the LLM-based and TPLM-based methods consistently outperform the others across various model settings, demonstrating their superiority.

In the RAG paradigm, while the LLM-based method still performs excellently, the Markdown method has shown unexpected effectiveness.

The varying frequency of domain-specific terms and verbs produced by these methods, alongside the differing quality of semantic representations in the generated text chunks, appear to be pivotal factors influencing performance disparities across the two systems.

The authors provide practical suggestions for choosing table-to-text methods based on the trade-offs between performance, resource requirements, and text diversity.

Stats

The text data in tables accounts for approximately 18% of the total content in the ICT-DATA dataset.

Quotes

"Table-to-text methods significantly impact the performance of QA systems, with relative score differences ranging from 2.8% to 9.0% in human evaluation and 4.8% to 16% in GPT-4 evaluation."
"In the DSFT paradigm, LLM-based and TPLM-based consistently outperform others across various model settings, demonstrating their superiority."
"In the RAG paradigm, while the LLM-based method still performs excellently, the Markdown has shown unexpected effectiveness."

Key Insights Distilled From

Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data

by Dehai Min,Na... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2402.12869.pdf

Deeper Inquiries

What other factors, beyond the ones discussed, could potentially influence the performance of LLM-based QA systems when leveraging domain hybrid data?

In addition to the factors already discussed, several other elements could impact the performance of LLM-based QA systems when dealing with domain hybrid data. One crucial factor is the quality and quantity of the training data used to fine-tune the language models. The diversity, relevance, and representativeness of the data can significantly affect the model's ability to understand and generate accurate responses. Furthermore, the complexity and structure of the tables in the hybrid data, as well as the level of noise or inconsistencies within the data, can also influence the model's performance. Additionally, the design of the retrieval mechanisms, the efficiency of the indexing process, and the relevance of the retrieved information to the query can play a vital role in the overall system performance.

How can the table-to-text generation methods be further improved to better suit the needs of domain-specific QA systems?

To enhance the suitability of table-to-text generation methods for domain-specific QA systems, several improvements can be considered. Firstly, incorporating domain-specific knowledge and vocabulary into the generation process can help ensure that the generated text aligns more closely with the domain context. Additionally, developing more advanced models that can capture the nuanced relationships and structures present in hybrid data, such as incorporating graph-based representations or attention mechanisms tailored to tables, can improve the quality of the generated text. Moreover, exploring multi-modal approaches that combine text and table information seamlessly and refining the fine-tuning process to adapt the models more effectively to the domain data can further enhance the performance of these methods.

How might the findings of this study apply to other domains beyond ICT, and what additional considerations would be necessary when exploring those domains?

The findings of this study can be extrapolated to other domains beyond ICT by considering the specific characteristics and requirements of those domains. When applying these findings to other domains, it is essential to consider the unique data structures, terminology, and knowledge patterns present in the new domain. Additionally, adapting the table-to-text methods and LLM-based QA systems to the specific needs and challenges of the target domain is crucial. This may involve customizing the training data, fine-tuning strategies, and retrieval mechanisms to align with the domain-specific nuances. Furthermore, considering the ethical, legal, and privacy implications of working with domain data from sensitive or regulated industries is essential when exploring new domains.