Tag
This paper investigates how similar large language model uncertainty is to human uncertainty, exploring alignment, calibration, and activation patterns in LLMs across multiple datasets and the impact of instruction fine-tuning.
This paper introduces MiA-Signature, a compressed representation of global activation patterns in LLMs designed to improve long-context understanding. It proposes using submodular-based selection to approximate full activation states, yielding performance gains in RAG and agentic systems.
LongAct proposes a saliency-guided sparse update strategy for improving long-context reasoning in LLMs by selectively updating weights associated with high-magnitude activations in query and key vectors, achieving ~8% improvement on LongBench v2.