Papers by Yevgeniy Vorobeychik
Protecting Language Models Against Unauthorized Distillation through Trace Rewriting (2026.acl-long)
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| Challenge: | Knowledge distillation is a widely adopted technique for transferring capabilities from LLMs to smaller, more efficient student models. |
| Approach: | They propose methods for modifying teacher-generated reasoning traces to achieve two objectives that deter unauthorized distillation: (1) anti-distillation, or degrading the training usefulness of query responses; and (2) API watermarking, which embeds verifiable signatures in student models. |
| Outcome: | The proposed method achieves strong anti-distillation effect while maintaining or even improving teacher performance. |
EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models (2025.emnlp-main)
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Han Liu, Ruoyao Wen, Srijith Nair, Jia Liu, Wenjing Lou, Chongjie Zhang, William Yeoh, Yevgeniy Vorobeychik, Ning Zhang
| Challenge: | Recurrent exchange of model updates in FL can result in prohibitively high communication costs, hindering the distributed learning process. |
| Approach: | They propose a federated fine-tuning framework that uses a round-robin segment sharing scheme to reduce network bandwidth and adaptive sparsification methods tailored to LoRA’s training dynamics. |
| Outcome: | The proposed framework reduces communication overhead without compromising performance on question-answering and value-alignment tasks. |
Mind the (DH) Gap! A Contrast in Risky Choices Between Reasoning and Conversational LLMs (2026.acl-long)
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| Challenge: | Large language models are increasingly used in decision support systems and workflows . traditional computational paradigms for decision-making under uncertainty choose an option that maximizes expected utility or payoff . |
| Approach: | They compare large language models as decision support systems and agentic workflows . they find that LLMs cluster into reasoning models and conversational models . |
| Outcome: | The proposed models differ in their ability to perform tasks and their ability in a human-like way. |
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models (2024.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have significantly enhanced the capabilities in natural language processing. |
| Approach: | They propose a method to poison large language models by using annotators to rank a set of collected responses to generate longer tokens. |
| Outcome: | The proposed method can generate longer tokens without harming the original safety alignment performance. |