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Exploring Generative Process Reward Modeling for Semi-Structured Data: A Case Study of Table Question Answering
Lei Tang, Wei Zhou, Mohsen Mesgar
Process reward models (PRMs) enhance complex reasoning in large language models (LLMs) by evaluating candidate solutions step-by-step and selecting answers based on aggregated step…
Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation
Wei Zhou, Bolei Ma, Annemarie Friedrich +1
Table Question Answering (TQA) aims to answer natural language questions about tabular data, often accompanied by additional contexts such as text passages. The task spans diverse…
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…
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…