3 papers
cs.CL2025
Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data
Shiping Yang, Jie Wu, Wenbiao Ding +7
Robustness has become a critical attribute for the deployment of RAG systems in real-world applications. Existing research focuses on robustness to explicit noise (e.g., document s…
cs.CL2025
MuDAF: Long-Context Multi-Document Attention Focusing through Contrastive Learning on Attention Heads
Weihao Liu, Ning Wu, Shiping Yang +4
Large Language Models (LLMs) frequently show distracted attention due to irrelevant information in the input, which severely impairs their long-context capabilities. Inspired by re…
cs.CL2024
Selected Languages are All You Need for Cross-lingual Truthfulness Transfer
Weihao Liu, Ning Wu, Wenbiao Ding +3
Truthfulness stands out as an essential challenge for Large Language Models (LLMs). Although many works have developed various ways for truthfulness enhancement, they seldom focus…