Papers by Mohammad-Reza Namazi-Rad
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
| Approach: | They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge. |
| Outcome: | The proposed models can be used to perform various tasks directly through in-context learning or for further fine-tuning for domain-specific uses. |
CITB: A Benchmark for Continual Instruction Tuning (2023.findings-emnlp)
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| Challenge: | Existing methods for instruction tuning do not leverage the rich natural language instructions. |
| Approach: | They propose to use a benchmark to study how instruction tuning works in CL tasks. |
| Outcome: | The proposed method can achieve similar or better results than existing CL methods. |
Turn-Level Active Learning for Dialogue State Tracking (2023.emnlp-main)
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| Challenge: | Existing approaches to annotate dialogues require supervised training, which requires human workers to manually annotates dialogues. |
| Approach: | They propose a turn-level active learning framework to actively select dialogue turns to annotate . their approach can achieve comparable performance to traditional training approaches . |
| Outcome: | The proposed model achieves comparable performance to existing training approaches with significantly less annotated data. |