5 papers
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…
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…
Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges
Bolei Ma, Yuting Li, Wei Zhou +7
Understanding pragmatics-the use of language in context-is crucial for developing NLP systems capable of interpreting nuanced language use. Despite recent advances in language tech…
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…