3 citations · 3 across the 7 of their papers we have counts for
7 papers · 1 filter
Less Experts, Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts
Jincheng Xie, Runheng Liu, Heyan Huang +4
Sparse Mixture-of-Experts (MoE) models have become an important approach for scaling Large Language Models (LLMs), but their inference efficiency depends strongly on expert activat…
AdaPLD: Adaptive Retrieval and Reuse for Efficient Model-Free Speculative Decoding
Runheng Liu, Jincheng Xie, Wen Hu +2
Speculative decoding accelerates generation by verifying multiple drafted tokens in a single target-model forward pass, reducing sequential decoding iterations. Model-free variants…
Zero-Shot Detection of LLM-Generated Text via Implicit Reward Model
Runheng Liu, Heyan Huang, Xingchen Xiao +1
Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their ability to generate human-like text has raised concerns about potential…
MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation
Xingchen Xiao, Heyan Huang, Runheng Liu +1
Large language models (LLMs) are widely used in retrieval-augmented generation (RAG) to incorporate external knowledge at inference time. However, when retrieved contexts are noisy…
Training-free Truthfulness Detection via Sparse MLP Value Vectors
Runheng Liu, Heyan Huang, Xingchen Xiao +2
Large language models (LLMs) are prone to generating factually incorrect content, motivating methods for assessing truthfulness from internal model signals. While supervised probin…
FlashBack: Efficient Retrieval-Augmented Language Modeling for Fast Inference
Runheng Liu, Xingchen Xiao, Heyan Huang +2
Retrieval-Augmented Language Modeling (RALM) by integrating large language models (LLM) with relevant documents from an external corpus is a proven method for enabling the LLM to g…