Papers with root-mean-square error (
ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models (2025.findings-naacl)
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| Challenge: | Performance prediction is a method to estimate the performance of Language Models (LMs) on various Natural Language Processing (NLP) tasks. |
| Approach: | They propose a task- and language-agnostic framework to predict the performance of Language Models (LMs) using proxy models. |
| Outcome: | The proposed framework outperforms the state-of-the-art in root-mean-square error (RMSE) and other robustness tests on multilingual NLP tasks. |
What just happened? Evaluating retrofitted distributional word vectors (N19-1)
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| Challenge: | Recent work has attempted to enhance vector space representations using information from structured semantic resources. |
| Approach: | They propose a root-mean-square error evaluation metric to evaluate the utility of different lexical resources for retrofitting. |
| Outcome: | The proposed method improves word similarity performance by using root-mean-square error (RMSE) and root-macro-error (RMME) metric. |