Hybrid Graphs for Table-and-Text based Question Answering using LLMs
This addresses the challenge of multi-source table-and-text question answering for AI applications, offering a novel method that improves performance and efficiency in a zero-shot setting.
The paper tackles the problem of answering questions that require reasoning across both tables and text by proposing a hybrid graph-based approach using LLMs without fine-tuning, achieving up to 10% improvement in Exact Match scores on Hybrid-QA and 5.4% on OTT-QA while reducing token usage by up to 53%.
Answering questions that require reasoning and aggregation across both structured (tables) and unstructured (raw text) data sources presents significant challenges. Current methods rely on fine-tuning and high-quality, human-curated data, which is difficult to obtain. Recent advances in Large Language Models (LLMs) have shown promising results for multi-hop question answering (QA) over single-source text data in a zero-shot setting, yet exploration into multi-source Table-Text QA remains limited. In this paper, we present a novel Hybrid Graph-based approach for Table-Text QA that leverages LLMs without fine-tuning. Our method constructs a unified Hybrid Graph from textual and tabular data, pruning information based on the input question to provide the LLM with relevant context concisely. We evaluate our approach on the challenging Hybrid-QA and OTT-QA datasets using state-of-the-art LLMs, including GPT-3.5, GPT-4, and LLaMA-3. Our method achieves the best zero-shot performance on both datasets, improving Exact Match scores by up to 10% on Hybrid-QA and 5.4% on OTT-QA. Moreover, our approach reduces token usage by up to 53% compared to the original context.