1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CL2025
VOCABTRIM: Vocabulary Pruning for Efficient Speculative Decoding in LLMs
Raghavv Goel, Sudhanshu Agrawal, Mukul Gagrani +9
In this paper, we introduce a simple training-free technique to improve the performance of drafter-based speculative decoding (SpD) methods that incorporates language modeling head…
cs.IR2023★ 1 cited
ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation
Junyan Qiu, Haitao Wang, Zhaolin Hong +3
The successful integration of large language models (LLMs) into recommendation systems has proven to be a major breakthrough in recent studies, paving the way for more generic and…