Papers with fine-tuning Language Models
HABERTOR: An Efficient and Effective Deep Hatespeech Detector (2020.emnlp-main)
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| Challenge: | HABERTOR model is a highly efficient and effective alternative to BERT for the hatespeech classification task. |
| Approach: | They propose to modify BERT's HABERTOR model to generate its own vocabularies and pre-trained it using the largest scale hatespeech dataset. |
| Outcome: | The proposed model is faster, more efficient and more robust than existing methods for hatespeech classification. |
Fine-tuning with HED-IT: The impact of human post-editing for dialogical language models (2024.findings-acl)
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Daniela Occhipinti, Michele Marchi, Irene Mondella, Huiyuan Lai, Felice Dell’Orletta, Malvina Nissim, Marco Guerini
| Challenge: | a recent study has focused on the quality of data generated by automatic methods for fine-tuning Language Models in languages less resourced than English. |
| Approach: | They investigate whether human intervention improves the quality of machine-generated dialogues . they use a large-scale dataset to fine-tune three different sizes of an LM . |
| Outcome: | The results show that human intervention can improve the quality of training data . larger models are less sensitive to data quality, while smaller models are more sensitive . |