Papers with endow models

3 papers
OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering (2022.naacl-main)

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Challenge: a table-based question answering system requires complex reasoning and alignment between questions and tables.
Approach: They propose a table-based QA model that consumes both natural and synthetic data . they combine retrieval with masking to pair natural sentences with QA .
Outcome: The proposed model outperforms existing models in few-shot and full settings and on WikiTableQuestions.
How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study (2026.acl-long)

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Challenge: Existing studies on spatial intelligence from the perspective of visual-spatial intelligence have not explored whether visual intelligence alone is sufficient to endow models with spatial intelligence.
Approach: They propose to use a linguistic perspective to investigate spatial intelligence from a theoretical perspective.
Outcome: The proposed model performs poorly on the proposed dataset while human can easily achieve 100% accuracy.
Development of Cognitive Intelligence in Pre-trained Language Models (2024.emnlp-main)

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Challenge: Recent studies show evidence for emergent cognitive abilities in Large Pre-trained Language Models (PLMs). Prior research into emergental cognitive abilities of PLMs has been path-independent to model training.
Approach: They use four task categories to examine the alignment of ten popular families of PLMs and evaluate their performance to the developmental trajectories of children's thinking.
Outcome: The results show that the models are more aligned to children's thinking than previous studies.

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