Papers by Manuel Torralbo
Fine-Tuning Medium-Scale LLMs for Joint Intent Classification and Slot Filling: A Data-Efficient and Cost-Effective Solution for SMEs (2025.coling-industry)
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| Challenge: | Current techniques for user comprehension in DS depend heavily on labeled data and the data annotation process for NLU is labor-intensive and requires expert annotators. |
| Approach: | They propose to fine-tune a model for joint Intent Classification and Slot Filling with only 10% of the data. |
| Outcome: | The proposed model outperforms existing models in monolingual and cross-lingual scenarios with only 10% of the data. |