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University of Illinois Urbana-Champaign
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LLMs struggle with adaptive planning, achieving only 67.75% accuracy when faced with progressively revealed world and user constraints.
Even state-of-the-art LLMs struggle with persuasion, but incorporating user profiles can significantly enhance their effectiveness by over 18%.
LMMs can't MacGyver their way out of a paper bag: they struggle to creatively repurpose objects in visually complex environments, revealing a critical gap in grounded reasoning beyond pattern recognition.
Extracting user profiles from recommendation lists is now more accurate thanks to RAPI, a new framework that leverages BERT embeddings and sample augmentation to boost inference accuracy by dynamically weighting user characteristics.
