17 citations · 25 across the 9 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
Neighboring Words Affect Human Interpretation of Saliency Explanations
Alon Jacovi, Hendrik Schuff, Heike Adel +2
Word-level saliency explanations ("heat maps over words") are often used to communicate feature-attribution in text-based models. Recent studies found that superficial factors such…
SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains
Koustava Goswami, Lukas Lange, Jun Araki +1
Prompting pre-trained language models leads to promising results across natural language processing tasks but is less effective when applied in low-resource domains, due to the dom…
CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain
Lukas Lange, Heike Adel, Jannik Strötgen +1
The field of natural language processing (NLP) has recently seen a large change towards using pre-trained language models for solving almost any task. Despite showing great improve…