Teaching-Assistant-in-the-Loop: Improving Knowledge Distillation from Imperfect Teacher Models in Low-Budget Scenarios (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated state-of-the-art (SOTA) performance across a wide spectrum of tasks. |
| Approach: | They propose a framework that leverages three signal types to improve efficiency within resource-constrained, imperfect teacher scenarios. |
| Outcome: | The proposed framework improves on four complex reasoning tasks by 20.79% compared to fine-tuning without any signals across datasets. |
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| Challenge: | Existing knowledge distillation schemes focus on a teacher as a source of knowledge and a gauge to detect miscalibration of a student. |
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| Challenge: | Large language models (LLMs) show strong reasoning and decision-making ability, but their high inference cost motivates transferring agentic skills to small language models. |
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