5 papers · 1 filter
ConFu: Contemplate the Future for Better Speculative Sampling
Zongyue Qin, Raghavv Goel, Mukul Gagrani +3
Speculative decoding has emerged as a powerful approach to accelerate large language model (LLM) inference by employing lightweight draft models to propose candidate tokens that ar…
Efficient Training-Free Multi-Token Prediction via Embedding-Space Probing
Raghavv Goel, Mukul Gagrani, Mingu Lee +1
Large Language Models (LLMs) possess latent multi-token prediction (MTP) abilities despite being trained only for next-token generation. We introduce ESP (Embedding-Space Probing),…
A Comparative analysis of Layer-wise Representational Capacity in AR and Diffusion LLMs
Raghavv Goel, Risheek Garrepalli, Sudhanshu Agrawal +3
Autoregressive (AR) language models build representations incrementally via left-to-right prediction, while diffusion language models (dLLMs) are trained through full-sequence deno…
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…
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-…