QASE Enhanced PLMs: Improved Control in Text Generation for MRC
QASE module enhances generative PLMs for better MRC performance.
KeNet: Knowledge-Enhanced Doc-Label Attention Network for Multi-Label Text Classification
외부 지식을 활용한 KeNet 모델은 다중 레이블 텍스트 분류에서 우수한 성능을 보입니다.
IMPOSSIBLE DISTILLATION: Paraphrasing and Summarization Framework
IMPOSSIBLE DISTILLATION distills high-quality paraphrase datasets and models from low-quality LMs using paraphrastic proximity and critic-guided filtering.
QASE Enhanced PLMs: Improved Control in Text Generation for MRC
Generative PLMs can be enhanced with the lightweight QASE module to improve text generation quality and factual consistency in MRC tasks.
MiniLLM: Knowledge Distillation of Large Language Models
Reverse KLD is proposed for distilling LLM knowledge into smaller models, improving performance and reducing exposure bias.
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks
Instruction tuning improves the social understanding of large language models for social scientific tasks.
BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings
Incorporating backward dependencies in large language models enhances sentence embeddings.
Hyper-CL: Conditioning Sentence Representations with Hypernetworks
Efficiently conditioning sentence representations using hypernetworks and contrastive learning.
LoRA-SP: Streamlined Partial Parameter Adaptation for Efficient Fine-Tuning of Large Language Models
Selective parameter freezing in LoRA-SP optimizes fine-tuning efficiency without compromising model performance.
Knowledge-Enhanced Doc-Label Attention Network for Multi-Label Text Classification
The author introduces KeNet, a Knowledge-enhanced Doc-Label Attention Network, to address challenges in Multi-Label Text Classification by incorporating external knowledge and attention mechanisms.
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