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cs.CL2025
Kongzi: A Historical Large Language Model with Fact Enhancement
Jiashu Yang, Ningning Wang, Yian Zhao +6
Large Language Models (LLMs) can exhibit factual inaccuracies during long-chain reasoning or under misleading prompts, limiting their reliability in historical analysis. We propose…
cs.CL2025
Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language Models
Hongcheng Guo, Juntao Yao, Boyang Wang +5
Mixture-of-Experts (MoE) architectures have emerged as a promising paradigm for scaling large language models (LLMs) with sparse activation of task-specific experts. Despite their…
cs.SE2025
DependEval: Benchmarking LLMs for Repository Dependency Understanding
Junjia Du, Yadi Liu, Hongcheng Guo +4
While large language models (LLMs) have shown considerable promise in code generation, real-world software development demands advanced repository-level reasoning. This includes un…