Papers by Kuzma Khrabrov

2 papers
Two Steps from Hell: Compositionality on Chemical LMs (2025.findings-emnlp)

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Challenge: Experiments with state-of-the-art ChemLLMs show significant performance drops in compositional tasks, highlighting the need for models that move beyond pattern recognition.
Approach: They introduce a benchmark to evaluate chemical language models' understanding of chemical language by identifying and analyzing compositional patterns within chemical data.
Outcome: The proposed benchmark shows that existing LLMs can handle complex queries without pattern recognition.
Lost in Translation: Chemical Language Models and the Misunderstanding of Molecule Structures (2024.findings-emnlp)

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Challenge: chemistry and natural language processing (NLP) have advanced drug discovery.
Approach: They propose a framework for assessment of Chemistry LMs of different natures that relies on augmentations that preserve an underlying chemical.
Outcome: The proposed framework relies on augmentations that preserve an underlying chemical, such as kekulization and cycle replacements.

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