5 citations · 13 across the 36 of their papers we have counts for
53 papers
SFAD: Speculative Factuality-Aware Decoding
Guanqiao Chen, Di Wang, Lijie Hu
As one of the most critical challenges in large language models, contextual faithfulness directly determines their reliability in knowledge-intensive applications. This task is par…
Algorithmic Recourse of In-Context Learning for Tabular Data
Wenshuo Dong, Jiaming Zhang, Shaopeng Fu +3
As predictive models are increasingly deployed in high-stakes settings such as credit approval, there is a growing need for post-hoc methods that provide recourse to affected indiv…
Predicting LLM Output Length via Entropy-Guided Representations
Huanyi Xie, Yubin Chen, Liangyu Wang +2
The long-tailed distribution of sequence lengths in LLM serving and reinforcement learning (RL) sampling causes significant computational waste due to excessive padding in batched…
Concept-Based Dictionary Learning for Inference-Time Safety in Vision Language Action Models
Siqi Wen, Shu Yang, Shaopeng Fu +3
Vision Language Action (VLA) models close the perception action loop by translating multimodal instructions into executable behaviors, but this very capability magnifies safety ris…
Global Evolutionary Steering: Refining Activation Steering Control via Cross-Layer Consistency
Xinyan Jiang, Wenjing Yu, Di Wang +1
Activation engineering enables precise control over Large Language Models (LLMs) without the computational cost of fine-tuning. However, existing methods deriving vectors from stat…
Understanding the Dynamics of Demonstration Conflict in In-Context Learning
Difan Jiao, Di Wang, Lijie Hu
In-context learning enables large language models to perform novel tasks through few-shot demonstrations. However, demonstrations per se can naturally contain noise and conflicting…