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cs.CL2026
Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs
Mingyu Lee, Akshat Ramachandran, Souvik Kundu +1
The inference efficiency of diffusion large language models (dLLMs) is constrained by two challenges: bidirectional attention precludes efficient KV-cache reuse, while increasing d…
cs.CL2024★ 2 cited
Understanding the Performance and Estimating the Cost of LLM Fine-Tuning
Yuchen Xia, Jiho Kim, Yuhan Chen +4
Due to the cost-prohibitive nature of training Large Language Models (LLMs), fine-tuning has emerged as an attractive alternative for specializing LLMs for specific tasks using lim…