CLSGen is a dual-head LLM fine-tuning framework that enables joint probabilistic classification and verbalized explanation generation without catastrophic forgetting of generative capabilities.
Large language models in medicine.Nature medicine, 29(8):1930–1940
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BoHA partitions frozen weights into a b by b grid and applies independent low-rank Hadamard factors per block, outperforming LoRA on matched-budget single-task averages while retaining 57.66% first-stage accuracy in a commonsense-to-arithmetic continual-learning test on Llama-3.2-3B.
citing papers explorer
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CLSGen: A Dual-Head Fine-Tuning Framework for Joint Probabilistic Classification and Verbalized Explanation
CLSGen is a dual-head LLM fine-tuning framework that enables joint probabilistic classification and verbalized explanation generation without catastrophic forgetting of generative capabilities.
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BoHA: Blockwise Hadamard Product Adaptation for Parameter-Efficient Fine-Tuning
BoHA partitions frozen weights into a b by b grid and applies independent low-rank Hadamard factors per block, outperforming LoRA on matched-budget single-task averages while retaining 57.66% first-stage accuracy in a commonsense-to-arithmetic continual-learning test on Llama-3.2-3B.
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