Papers by Arthur Brazinskas
Small Language Models Improve Giants by Rewriting Their Outputs (2024.eacl-long)
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| Challenge: | despite impressive performance of large language models, they lag behind specialized models in various tasks. |
| Approach: | They propose a training model that can be integrated with different LLMs at inference to improve their performance without task-specific training. |
| Outcome: | The proposed model outperforms standard models on four natural language generation tasks. |
Efficient Few-Shot Fine-Tuning for Opinion Summarization (2022.findings-naacl)
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| Challenge: | Abstractive summarization models are typically pre-trained on large amounts of generic texts . large annotated datasets of reviews paired with reference summaries are not available . |
| Approach: | They propose a few-shot method which uses adapters to store in-domain knowledge . they pre-train adapters on unannotated customer reviews and fine-tune them on annotated datasets . |
| Outcome: | The proposed method can store in-domain knowledge and improves on large annotated reviews . it improves coherence and redundancies on the Amazon and Yelp datasets . |