Papers by Zhexiong Liu
Efficient Layer-wise LLM Fine-tuning for Revision Intention Prediction (2025.findings-emnlp)
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| Challenge: | Large Language Models have shown extraordinary success across text generation tasks . however, their potential for simple yet essential text classification remains underexplored . |
| Approach: | a plug-and-play layer-wise parameter-efficient fine-tuning framework is proposed . it fine- tunes a subset of important LLM layers while freezing redundant ones . |
| Outcome: | a plug-and-play framework fine-tunes a subset of important LLM layers while freezing redundant layers. |
Intention-Adaptive LLM Fine-Tuning for Text Revision Generation (2026.findings-eacl)
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| Challenge: | Existing work on large language models (LLMs) has demonstrated impressive capabilities in context-based text generation tasks, such as summarization and reasoning. |
| Approach: | They propose an intention-adaptive layer-wise LLM fine-tuning framework that dynamically selects a subset of LLM layers to learn intentions and transfers them to revision generation. |
| Outcome: | The proposed framework outperforms PEFT baselines on small revision corpora while maintaining fast convergence and accuracy. |