MIST models up to 10x larger than prior work, fine-tuned on over 400 structure-property tasks, match or exceed SOTA on benchmarks and demonstrate zero-shot olfactory perception mapping consistent with hyperbolic geometry.
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Performance gaps in multilingual LMs frequently arise from modeling choices such as tokenization and data exposure rather than intrinsic linguistic complexity.
citing papers explorer
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Foundation Models for Discovery and Exploration in Chemical Space
MIST models up to 10x larger than prior work, fine-tuned on over 400 structure-property tasks, match or exceed SOTA on benchmarks and demonstrate zero-shot olfactory perception mapping consistent with hyperbolic geometry.
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The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?
Performance gaps in multilingual LMs frequently arise from modeling choices such as tokenization and data exposure rather than intrinsic linguistic complexity.