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Martin Pömsl

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The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources
Akshatha Arodi
Martin Pömsl
Kaheer Suleman
Adam Trischler
A.R. Olteanu
Jackie CK Cheung
Many state-of-the-art natural language understanding (NLU) models are based on pretrained neural language models. These models often make in… (see more)ferences using information from multiple sources. An important class of such inferences are those that require both background knowledge, presumably contained in a model’s pretrained parameters, and instance-specific information that is supplied at inference time. However, the integration and reasoning abilities of NLU models in the presence of multiple knowledge sources have been largely understudied. In this work, we propose a test suite of coreference resolution subtasks that require reasoning over multiple facts. These subtasks differ in terms of which knowledge sources contain the relevant facts. We also introduce subtasks where knowledge is present only at inference time using fictional knowledge. We evaluate state-of-the-art coreference resolution models on our dataset. Our results indicate that several models struggle to reason on-the-fly over knowledge observed both at pretrain time and at inference time. However, with task-specific training, a subset of models demonstrates the ability to integrate certain knowledge types from multiple sources. Still, even the best performing models seem to have difficulties with reliably integrating knowledge presented only at inference time.
2023-06-30
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)
doi.org
arxiv.org
The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources
Akshatha Arodi
Martin Pömsl
Kaheer Suleman
Adam Trischler
Alexandra Olteanu
Jackie Chi Kit Cheung
Akshatha Arodi, Martin Pömsl, Kaheer Suleman, Adam Trischler, Alexandra Olteanu, Jackie Chi Kit Cheung. Proceedings of the 61st Annual Meet… (see more)ing of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
2022-12-31
Association for Computational Linguistics (published)
doi.org
arxiv.org