3 papers
cs.CL2026
Ultra-Low-Dimensional Prompt Tuning via Random Projection
Zijun Wu, Yongchang Hao, Lili Mou
Large language models achieve state-of-the-art performance but are increasingly costly to fine-tune. Prompt tuning is a parameter-efficient fine-tuning method that addresses parame…
cs.LG2026
Cactus: Accelerating Auto-Regressive Decoding with Constrained Acceptance Speculative Sampling
Yongchang Hao, Lili Mou
Speculative sampling (SpS) has been successful in accelerating the decoding throughput of auto-regressive large language models by leveraging smaller draft models. SpS strictly enf…
cs.CL2026
TokMem: One-Token Procedural Memory for Large Language Models
Zijun Wu, Yongchang Hao, Lili Mou
Large language models are typically controlled via prompts, which must be repeatedly re-processed for every new query and are difficult to reuse modularly. We introduce TokMem, a p…