Blog(1)

• Efficient Compositional Multi-tasking for On-device Large Language Models

  Adapter parameters [2] provide a mechanism to modify the behaviour of machine learning models and have gained significant popularity in the context of large language models (LLMs) and generative AI.

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Publications(22)

• Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference

  AuthorRui Li, Stylianos I. Venieris

  PublishedConference on Empirical Methods in Natural Language Processing (EMNLP)

  Date2025-11-05

• Efficient Compositional Multi-tasking for On-device Large Language Models

  AuthorOndrej Bohdal, Mete Ozay, Jijoong Moon, Kyeng-Hun Lee, Hyeonmok Ko

  PublishedThe 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP)

  Date2025-11-04

• On-device System of Compositional Multi-tasking in Large Language Models

  AuthorOndrej Bohdal, Kyeng-Hun Lee, Jijoong Moon, Hyeonmok Ko, Mete Ozay

  PublishedThe 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP Industry Track)

  Date2025-11-04

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News(1)

• Samsung Research at EMNLP 2018

  Research from Samsung Research will be presented in the oral and poster sessions of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018).

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