35 citations · 57 across the 14 of their papers we have counts for
5 papers · 1 filter
Surfing the Activation Wave: Analyzing the Information and Geometry of LLM Hidden States for Speculative Decoding
Raghavv Goel, Sudhanshu Agrawal, Mukul Gagrani +2
Decoder hidden states from different layers of large language models (LLMs) often encode complementary information. However, existing speculative decoding methods typically rely on…
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),…
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-…