DABS is a single-pass framework that builds a depth-ordered substrate from one Transformer encoding and performs lightweight aspect-conditioned readout, cutting computation by up to 60% on multi-aspect ATSA benchmarks while matching prior accuracy.
L ay A lign: Enhancing Multilingual Reasoning in Large Language Models via Layer-Wise Adaptive Fusion and Alignment Strategy
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GIFT guides adapter fine-tuning on base models with confidence signals from instruction-tuned models before merging, yielding task-specialized models that outperform direct fine-tuning on math and knowledge benchmarks.
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Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis
DABS is a single-pass framework that builds a depth-ordered substrate from one Transformer encoding and performs lightweight aspect-conditioned readout, cutting computation by up to 60% on multi-aspect ATSA benchmarks while matching prior accuracy.
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GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models
GIFT guides adapter fine-tuning on base models with confidence signals from instruction-tuned models before merging, yielding task-specialized models that outperform direct fine-tuning on math and knowledge benchmarks.