Papers with supervised fine-tuning approaches
Multilingual Fine-Grained News Headline Hallucination Detection (2024.findings-emnlp)
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| Challenge: | Existing models to generate news headlines often suffer from the "hallucination" problem, where the produced headline does not fully align with the source article's content. |
| Approach: | They propose to use a multilingual, fine-grained dataset to detect news headlines in 5 languages using supervised fine-tuning techniques and coarse-to-fine prompting to boost the few-shot detection performance. |
| Outcome: | The proposed methods boost the few-shot hallucination detection performance in terms of the example-F1 metric. |
HMCL: Task-Optimal Text Representation Adaptation through Hierarchical Contrastive Learning (2025.findings-emnlp)
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| Challenge: | Hierarchical Multilevel Contrastive Learning (HMCL) improves text representation for general large language models. |
| Approach: | a new contrastive learning framework is developed to improve general large language models . HMCL integrates 3-level semantic differentiation and unifies contrastive and pair classification into a strategy . |
| Outcome: | HMCL outperforms unsupervised methods and supervised fine-tuning approaches in multi-domain and multilingual benchmarks. |