TTQA-RS- A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization
<|reference_start|>TTQA-RS- A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization: Question answering (QA) over tables and text has gained much popularity over the years. Multi-hop table-text QA requires multiple hops between the table and text, making it a challenging QA task. Although several works have attempted to solve the table-text QA task, most involve training the models and requiring labeled data. In this paper, we have proposed a Retrieval Augmented Generation (RAG) based model - TTQA-RS: A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization. Our model uses an enhanced retriever for table-text information retrieval and uses augmented knowledge, including table-text summary with decomposed sub-questions with answers for a reasoning-based table-text QA. Using open-source language models, our model outperformed all existing prompting methods for table-text QA tasks on existing table-text QA datasets, such as HybridQA and OTT-QA's development set. Our experiments demonstrate the potential of prompt-based approaches using open-source LLMs. Additionally, by using LLaMA3-70B, our model achieved state-of-the-art performance for prompting-based methods on multi-hop table-text QA.<|reference_end|>
@article{bardhan2024ttqa-rs-,
    title={TTQA-RS- A break-down prompting approach for Multi-hop Table-Text
  Question Answering with Reasoning and Summarization},
    author={Jayetri Bardhan, Bushi Xiao, Daisy Zhe Wang},
    journal={arXiv preprint arXiv:2406.14732},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14732},
    primaryClass={cs.CL cs.IR}
}