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. |
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