Prefix-tuning matches or exceeds fine-tuning on NLG tasks by optimizing a continuous prefix using 0.1% of parameters while keeping the LM frozen.
Text Summarization with Pretrained Encoders
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
representative citing papers
Vocabulary adaptation via targeted token addition and replacement improves semantic similarity, domain word usage, and training efficiency for LLM summarization in legal and medical domains.
Ex2Bundle synthesizes package queries from example bundles using aggregate constraints and applies data-aware relaxation when constraints are infeasible, shown on focused text snippet extraction.
An evidence-based model generates queries from query-free datasets, yielding summaries with competitive ROUGE scores to those using original queries.
citing papers explorer
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Prefix-Tuning: Optimizing Continuous Prompts for Generation
Prefix-tuning matches or exceeds fine-tuning on NLG tasks by optimizing a continuous prefix using 0.1% of parameters while keeping the LM frozen.
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Learning Faster with Better Tokens: Parameter-Efficient Vocabulary Adaptation for Specialized Text Summarization
Vocabulary adaptation via targeted token addition and replacement improves semantic similarity, domain word usage, and training efficiency for LLM summarization in legal and medical domains.
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Example-Driven Intent Synthesis for Constrained Data Bundle Retrieval: Focused Text Snippet Extraction and Beyond
Ex2Bundle synthesizes package queries from example bundles using aggregate constraints and applies data-aware relaxation when constraints are infeasible, shown on focused text snippet extraction.
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Generating Query-Focused Summarization Datasets from Query-Free Summarization Datasets
An evidence-based model generates queries from query-free datasets, yielding summaries with competitive ROUGE scores to those using original queries.