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cs.CL2026
PAVE: Premise-Aware Validation and Editing for Retrieval-Augmented LLMs
Tianyi Huang, Caden Yang, Emily Yin +2
Retrieval-augmented language models can retrieve relevant evidence yet still commit to answers before explicitly checking whether the retrieved context supports the conclusion. We…
cs.CL2025
Enough Coin Flips Can Make LLMs Act Bayesian
Ritwik Gupta, Rodolfo Corona, Jiaxin Ge +4
Large language models (LLMs) exhibit the ability to generalize given few-shot examples in their input prompt, an emergent capability known as in-context learning (ICL). We investig…