1 citations · 2 across the 5 of their papers we have counts for
5 papers
Suan: Rectifying Direct Preference Safety Alignment in Large Language Models
Oleksandr Cherednichenko, Roman Klypa
Integrating robust safety guardrails into Large Language Models (LLMs) is essential for delivering helpful yet harmless responses. While proprietary systems exhibit reliable safety…
Multimodal Alignment and Preference Optimization for Zero-Shot Conditional RNA Generation
Roman Klypa, Alberto Bietti, Sergei Grudinin
The design of RNA molecules that interact with specific proteins is a critical challenge in experimental and computational biology. Despite recent progress in natural language mode…
Diversity in Large Language Models under Supervised Fine-Tuning
Roman Klypa, Oleksandr Cherednichenko
Supervised Fine-Tuning (SFT) is essential for aligning Large Language Models (LLMs) with user intent, yet it is believed to suppress generative diversity. Although this reduction i…
MIMIC: A Generative Multimodal Foundation Model for Biomolecules
Siavash Golkar, Jake Kovalic, Irina Espejo Morales +28
Biological function emerges from coupled constraints across sequence, structure, regulation, evolution, and cellular context, yet most foundation models in biology are trained with…
BAnG: Bidirectional Anchored Generation for Conditional RNA Design
Roman Klypa, Alberto Bietti, Sergei Grudinin
Designing RNA molecules that interact with specific proteins is a critical challenge in experimental and computational biology. Existing computational approaches require a substant…