6 papers
Beyond Bag-of-Patches: Learning Global Layout via Textual Supervision for Late-Interaction Visual Document Retrieval
Pascal Tilli, Mohsen Mesgar
Visual Document Retrieval (VDR) models mostly rely on late interaction architectures, in which documents are represented by a set of local patch embeddings and then matched against…
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…
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…
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…