most citedExplaining Pre-Trained Language Models with Attribution Scores: An Analysis in Low-Resource Settings

1 citations · 1 across the 6 of their papers we have counts for

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7 papers

cs.CL2025

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…

cs.CL2025

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…

cs.CV2024

Human vs. AI: A Novel Benchmark and a Comparative Study on the Detection of Generated Images and the Impact of Prompts

Philipp Moeßner, Heike Adel

With the advent of publicly available AI-based text-to-image systems, the process of creating photorealistic but fully synthetic images has been largely democratized. This can pose…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024★ 1 cited

Explaining Pre-Trained Language Models with Attribution Scores: An Analysis in Low-Resource Settings

Wei Zhou, Heike Adel, Hendrik Schuff +1

Attribution scores indicate the importance of different input parts and can, thus, explain model behaviour. Currently, prompt-based models are gaining popularity, i.a., due to thei…