Kyungkoo Min  Jooyoung Choi  Myoseop Sim  Minjun Park  Stanley Jungkyu Choi

10.5626/JOK.2024.51.10.892

table QA question answering GPT EXAONE containment matching

Tables in documents can be described as collections of information that condense and aggregate important data. Research on table question answering techniques focusing on querying such tables is underway, with studies utilizing language models showing promising results. This study applied an emerging generative language model technology to table question answering, examining outcomes based on changes in the language model and prompts. It analyzed results using methods suitable for short-answer and generative outcomes. Applying our custom-developed EXAONE 1.7B model to the KorWiki dataset yielded an EM of 92.49 and an F1 score of 94.81. This demonstrates that fine-tuning smaller models can achieve better performance than larger models such as GPT-4. Additionally, the EXAONE 25B model exhibited the best performance among tested models on the KorQuAD2Table dataset.