2 citations · 2 across the 11 of their papers we have counts for
4 papers · 1 filter
How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark
Ruizhong Qiu, Weiliang Will Zeng, James Ezick +2
The emergence of large language models (LLMs) has significantly pushed the frontiers of program synthesis. Advancement of LLM-based program synthesis calls for a thorough evaluatio…
On Speculative Decoding for Multimodal Large Language Models
Mukul Gagrani, Raghavv Goel, Wonseok Jeon +3
Inference with Multimodal Large Language Models (MLLMs) is slow due to their large-language-model backbone which suffers from memory bandwidth bottleneck and generates tokens auto-…
Recursive Speculative Decoding: Accelerating LLM Inference via Sampling Without Replacement
Wonseok Jeon, Mukul Gagrani, Raghavv Goel +3
Speculative decoding is an inference-acceleration method for large language models (LLMs) where a small language model generates a draft-token sequence which is further verified by…
Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs
Raghavv Goel, Mukul Gagrani, Wonseok Jeon +3
Text generation with Large Language Models (LLMs) is known to be memory bound due to the combination of their auto-regressive nature, huge parameter counts, and limited memory band…