3 papers
cs.CL2025
RefusalBench: Generative Evaluation of Selective Refusal in Grounded Language Models
Aashiq Muhamed, Leonardo F. R. Ribeiro, Markus Dreyer +2
The ability of language models in RAG systems to selectively refuse to answer based on flawed context is critical for safety, yet remains a significant failure point. Our large-sca…
cs.LG2025
Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs
Xiangchen Song, Aashiq Muhamed, Yujia Zheng +5
Sparse Autoencoders (SAEs) are a prominent tool in mechanistic interpretability (MI) for decomposing neural network activations into interpretable features. However, the aspiration…
cs.AI2025
CoRAG: Collaborative Retrieval-Augmented Generation
Aashiq Muhamed, Mona Diab, Virginia Smith
Retrieval-Augmented Generation (RAG) models excel in knowledge-intensive tasks, especially under few-shot learning constraints. We introduce CoRAG, a framework extending RAG to col…