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This paper compares the geometric structures induced by deep learning vector embeddings (CamemBERT) and lexical co-occurrence graph models on the French 'Great National Debate' corpus, finding similar local topology but distinct global organization, highlighting complementarity between the two approaches.
This paper proposes a framework using Supervised Semantic Differential to represent psychological constructs as directions in a shared word-embedding space, enabling comparison across different measurement instruments and research traditions.
Large-scale study of 15 LLMs across 8 tasks reveals that optimization success hinges on maintaining localized search trajectories rather than initial problem-solving ability or solution novelty.