Papers by Aldo Gangemi
Towards Distribution-shift Robust Text Classification of Emotional Content (2023.findings-acl)
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| Challenge: | Recent work has shown that supervised models are more robust to change in domain and distribution, but the decrease in performance due to the distribution shift is still a major issue for supervised systems. |
| Approach: | They propose to fine-tune supervised models on task-specific datasets to achieve out-of-distribution performance. |
| Outcome: | The proposed model outperforms all available models in distribution and out of distribution with only a few thousand training samples. |
GRAMMAR-LLM: Grammar-Constrained Natural Language Generation (2025.findings-acl)
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| Challenge: | Existing approaches to fine-tuning and prompting are insufficient to ensure compliance with predefined taxonomies, syntactic structures, or domain-specific rules. |
| Approach: | They propose a framework that integrates formal grammatical constraints into the decoding process to enforce syntactic correctness in linear time while maintaining expressiveness in grammar rule definition. |
| Outcome: | The proposed framework enforces syntactic correctness in linear time while maintaining expressiveness in grammar rule definition. |