2 papers
cs.CL2025
Mind the Gap: Aligning the Brain with Language Models Requires a Nonlinear and Multimodal Approach
Danny Dongyeop Han, Yunju Cho, Jiook Cha +1
Self-supervised language and audio models effectively predict brain responses to speech. However, traditional prediction models rely on linear mappings from unimodal features, desp…
cs.CL2024
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning
Seong-Il Park, Seung-Woo Choi, Na-Hyun Kim +1
Retrieval-Augmented Language Models (RALMs) have significantly improved performance in open-domain question answering (QA) by leveraging external knowledge. However, RALMs still st…