5 papers
Recursive Offline Distillation via Entropy Gain Sampling and Answer Mixing
Andrey Goncharov, Daniil Vyazhev, Simon Epanov +1
Distillation of reasoning chains from Large Language Models (LLMs) into their smaller siblings is a standard training approach. Frequently, researchers do not have full access to a…
Enhancing the Reliability of Medical AI through Expert-guided Uncertainty Modeling
Aleksei Khalin, Ekaterina Zaychenkova, Aleksandr Yugay +4
Artificial intelligence (AI) systems accelerate medical workflows and improve diagnostic accuracy in healthcare, serving as second-opinion systems. However, the unpredictability of…
Language steering in latent space to mitigate unintended code-switching
Andrey Goncharov, Nikolai Kondusov, Alexey Zaytsev
Multilingual Large Language Models (LLMs) often exhibit hallucinations such as unintended code-switching, reducing reliability in downstream tasks. We propose latent-space language…
Complexity-aware fine-tuning
Andrey Goncharov, Daniil Vyazhev, Petr Sychev +2
General-purpose Large Language Models (LLMs) are frequently fine-tuned through supervised fine-tuning (SFT) to enhance performance in specific domains. Better results can be achiev…
When an LLM is apprehensive about its answers -- and when its uncertainty is justified
Petr Sychev, Andrey Goncharov, Daniil Vyazhev +2
Uncertainty estimation is crucial for evaluating Large Language Models (LLMs), particularly in high-stakes domains where incorrect answers result in significant consequences. Numer…