5 papers · 1 filter
p2-TQA: A Process-based Preference Learning Framework for Self-Improving Table Question Answering Models
Wei Zhou, Mohsen Mesgar, Heike Adel +1
Table question answering (TQA) focuses on answering questions based on tabular data. Developing TQA systems targets effective interaction with tabular data for tasks such as cell r…
Texts or Images? A Fine-grained Analysis on the Effectiveness of Input Representations and Models for Table Question Answering
Wei Zhou, Mohsen Mesgar, Heike Adel +1
In table question answering (TQA), tables are encoded as either texts or images. Prior work suggests that passing images of tables to multi-modal large language models (MLLMs) perf…
Efficient Multi-Agent Collaboration with Tool Use for Online Planning in Complex Table Question Answering
Wei Zhou, Mohsen Mesgar, Annemarie Friedrich +1
Complex table question answering (TQA) aims to answer questions that require complex reasoning, such as multi-step or multi-category reasoning, over data represented in tabular for…
Learning Rules from KGs Guided by Language Models
Zihang Peng, Daria Stepanova, Vinh Thinh Ho +3
Advances in information extraction have enabled the automatic construction of large knowledge graphs (e.g., Yago, Wikidata or Google KG), which are widely used in many applications…
FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering
Wei Zhou, Mohsen Mesgar, Heike Adel +1
Table Question Answering (TQA) aims at composing an answer to a question based on tabular data. While prior research has shown that TQA models lack robustness, understanding the un…