Papers by Taku Sakamoto
Predicting Numerals in Text Using Nearest Neighbor Language Models (2023.findings-acl)
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| Challenge: | naive language models treat numerals as string tokens, resulting in difficulty in acquiring commonsense . kNN-LM is an extension of pre-trained neural LMs with the k-nearest neighbor (kNN) search . |
| Approach: | They apply k-nearest neighbor LM to a masked numeral prediction task . they found it is effective for fine-grained predictions of numerals from context . |
| Outcome: | The retrieval-based method is effective for fine-grained numeral prediction from context . it improves accuracy for the OOV numerals, the study shows . |
Development of Numerical Error Detection Tasks to Analyze the Numerical Capabilities of Language Models (2025.coling-main)
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| Challenge: | Existing language models are difficult to detect numerical errors because of their finite set of tokens. |
| Approach: | They use a benchmark dataset to classify numerical errors using automatically generated numerical errors and investigate their ability to detect errors. |
| Outcome: | The proposed model performs well in the numerical error detection task, but not as accurate as humans. |