Papers with mean absolute error
Enhancing Job Evaluation with Data Augmentation and Text Classification (2026.acl-industry)
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| Challenge: | Recruiters rely on job titles, role descriptions, and responsibility levels to determine job grades and salary structures. |
| Approach: | They propose to semi-automate job evaluation by fine-tuning a RoBERTa model for classification and using Gemini to generate synthetic job descriptions for rare job titles. |
| Outcome: | The proposed method improves job evaluation by boosting consistency and speeding up workflows. |
The Multilingual Amazon Reviews Corpus (2020.emnlp-main)
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| Challenge: | The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019 . |
| Approach: | They propose to use mean absolute error (MAE) instead of classification accuracy for this task since MAE accounts for ordinal nature of the ratings. |
| Outcome: | The proposed model uses mean absolute error (MAE) instead of classification accuracy since MAE accounts for ordinal nature of the ratings. |
Revisiting Sample Size Determination in Natural Language Understanding (2023.findings-acl)
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Ernie Chang, Muhammad Hassan Rashid, Pin-Jie Lin, Changsheng Zhao, Vera Demberg, Yangyang Shi, Vikas Chandra
| Challenge: | Recent work has sought to reduce the annotation costs through the use of active learning and data sampling. |
| Approach: | They propose to estimate the training sample size needed to achieve a targeted model performance based on small amount of training samples. |
| Outcome: | The proposed approach predicts model performance within a small margin of mean absolute error (0.9%) with only 10% data. |
LLM Performance Predictors are good initializers for Architecture Search (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have diverse applications, encompassing both open-ended tasks (e.g., brainstorming and chat) and closed-ended ones (eg. question answering). |
| Approach: | They construct PP prompts for Large Language Models (LLMs) that estimate the performance of specific deep neural network architectures on downstream tasks. |
| Outcome: | The proposed model achieves a SoTA mean absolute error and a slight degradation in rank correlation coefficient compared to baseline predictors in machine translation tasks. |
Optimising Twitter-based Political Election Prediction with Relevance andSentiment Filters (2020.lrec-1)
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| Challenge: | A set of over 17,000 tweets containing political party names were annotated by at least three annotators per tweet on ten features denoting communicative intent. |
| Approach: | They propose to annotate tweets containing political party names by using oracle filters to achieve lower MAEs. |
| Outcome: | The proposed method achieves a mean absolute error (MAE) of 2.71% for 2012 and 2.02% for 2012, and 2.89% for 2015 for the Dutch elections. |