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University of Pittsburgh, Pittsburgh, PA, USA
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STRIVE automates the generation of event plausibility sets, achieving a remarkable 75% quality rate, but still struggles with boundary cases that require human judgment.
Leveraging internal neuron activations, Neuron-OPSD achieves superior in-domain performance without the need for costly expert annotations.
Shifting the focus to internal neuron dynamics reveals that LLMs can be better adapted to specialized domains with fewer, more informative examples.
Instability in persona-driven generations can vary dramatically across model families and question types, with math and commonsense tasks proving particularly volatile.
LLMs struggle to replicate the nuanced feedback that instructors prioritize, with performance dropping as feedback complexity rises.
