3 papers
cs.CL2026
REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations
Buyun Liang, Jinqi Luo, Liangzu Peng +6
Large language models (LLMs) achieve strong performance across many tasks but remain vulnerable to hallucinations, making it important to systematically evaluate their reliability…
cs.IR2025
TCDE: Topic-Centric Dual Expansion of Queries and Documents with Large Language Models for Information Retrieval
Yu Yang, Feng Tian, Ping Chen
Query Expansion (QE) enriches queries and Document Expansion (DE) enriches documents, and these two techniques are often applied separately. However, such separate application may…
cs.CL2025
SC: Speculative Sampling with Syntactic and Semantic Coherence for Efficient Inference of Large Language Models
Tao He, Guang Huang, Yu Yang +5
Large language models (LLMs) exhibit remarkable reasoning capabilities across diverse downstream tasks. However, their autoregressive nature leads to substantial inference latency,…