Papers with stage framework
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. |
Improving Unsupervised Out-of-domain detection through Pseudo Labeling and Learning (2023.findings-eacl)
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| Challenge: | Unsupervised OOD detection is a task aimed at discriminating whether given samples are from the in-domain (IND) . previous studies adopted the one-class classification approach, assuming that the training samples come from a single domain. |
| Approach: | They propose a framework that leverages latent categorical information to improve representation learning for textual OOD detection. |
| Outcome: | The proposed framework significantly outperforms baseline models on three datasets. |
Zero-Shot Spoken Language Understanding via Large Language Models: A Preliminary Study (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have shown promising results in zero-shot settings, which motivates us to explore prompt-based methods. |
| Approach: | They propose a two-stage framework which transforms the SLU task into a question-answering problem by directly prompting LLMs. |
| Outcome: | The proposed framework can be built by directly prompting LLMs to understand user needs without training data. |