7 papers
SCOPE-Router: Cost-Aware Open-Set VLM Routing for Execution-Oriented Tasks
Tao Yu, Yifei Qu, Zhiqing Cui +14
Model routing aims to select the most suitable model from a candidate pool for each query, balancing quality and cost. Existing VLM routing research is limited to traditional VQA e…
FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models
Fan Mo, Yuxuan Han, Geng Zhang +2
Mixture-of-Experts (MoE) language models scale model ability with sparsely activated experts, making this architecture a standard recipe for modern large models. However, sparse ac…
Agent-as-a-Router: Agentic Model Routing for Coding Tasks
Pengfei Zhou, Zhiwei Tang, Yixing Ma +8
Real-world users typically have access to multiple Large Language Models (LLMs) from different providers, and these LLMs often excel at distinct domains, yet none dominate all. Con…
Optimizing Class-Level Probability Reweighting Coefficients for Equitable Prompting Accuracy
Ruixi Lin, Yang You
Even as we engineer LLMs for alignment and safety, they often uncover biases from pre-training data's statistical regularities (from disproportionate co-occurrences to stereotypica…
Ensemble Debiasing Across Class and Sample Levels for Fairer Prompting Accuracy
Ruixi Lin, Ziqiao Wang, Yang You
Language models are strong few-shot learners and achieve good overall accuracy in text classification tasks, masking the fact that their results suffer from great class accuracy im…
Let the Fuzzy Rule Speak: Enhancing In-context Learning Debiasing with Interpretability
Ruixi Lin, Yang You
Large language models (LLMs) often struggle with balanced class accuracy in text classification tasks using in-context learning (ICL), hindering some practical uses due to user dis…