Papers with IPT
How Does In-Context Learning Help Prompt Tuning? (2024.findings-eacl)
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| Challenge: | a growing number of parameter-efficient adaptation methods are needed to fine-tune large language models. |
| Approach: | They propose a method that combines prompt tuning and in-context learning to improve prompt tuning by concatenating a natural language demonstration with learned prompt embeddings. |
| Outcome: | The proposed method outperforms prompt tuning and prompt tuning on five language generation tasks. |
Comprehensive Study of Bilingual and Multi-category Instruction Pre-training (2026.findings-eacl)
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| Challenge: | Instruction pre-training (IPT) has recently gained attention as an intermediate stage between pre- and post-training for large language models. |
| Approach: | They study the optimal balance between raw and instruction-response data, languages, and task categories in an LLM instruction-respondence dataset. |
| Outcome: | The proposed model improves on English-centric and bilingual models using bilingual instruction-response datasets. |
Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation (2021.eacl-main)
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| Challenge: | Traditional NLP has long held (supervised) syntactic parsing necessary for successful higher-level semantic language understanding (LU). |
| Approach: | They empirically examine the usefulness of supervised parsing for semantic LU in LM-pretrained transformer networks. |
| Outcome: | The proposed model is based on LM-pretrained transformer networks with a biaffine parsing head and fine-tuned for LU tasks. |
Enhancing Nursing and Elderly Care with Large Language Models: An AI-Driven Framework (2025.coling-main)
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| Challenge: | Experimental results demonstrate significant improvements, paving the way for AI-driven solutions to meet the growing demands of healthcare in aging populations. |
| Approach: | They introduce a Chinese nursing dataset and implement incremental pre-training and supervised fine-tuning techniques to enhance LLM performance in specialized tasks. |
| Outcome: | The proposed model performs better in real-time patient monitoring and interaction tasks than previous models. |