3 papers
cs.CL2025
Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis
Junzhuo Li, Bo Wang, Xiuze Zhou +3
The interpretability of Mixture-of-Experts (MoE) models, especially those with heterogeneous designs, remains underexplored. Existing attribution methods for dense models fail to c…
cs.CL2025
Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities
Junyan Zhang, Yubo Gao, Yibo Yan +8
The finetuning of Large Language Models (LLMs) has significantly advanced their instruction-following capabilities, yet the underlying computational mechanisms driving these improv…
cs.CL2023
Language Representation Projection: Can We Transfer Factual Knowledge across Languages in Multilingual Language Models?
Shaoyang Xu, Junzhuo Li, Deyi Xiong
Multilingual pretrained language models serve as repositories of multilingual factual knowledge. Nevertheless, a substantial performance gap of factual knowledge probing exists bet…