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(19)

• 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

• Distilling Knowledge from Text-to-Image Generative Models Improves Visio-Linguistic Reasoning in CLIP

  AuthorShell Xu Hu

  PublishedConference on Empirical Methods in Natural Language Processing (EMNLP)

  Date2024-11-13

• MobileQuant: Mobile-friendly Quantization for On-device Language Models

  AuthorShell Xu Hu, Sourav Bhattacharya, Timothy Hospedales, Georgios Tzimiropoulos, Brais Martinez

  PublishedConference on Empirical Methods in Natural Language Processing (EMNLP)

  Date2024-11-13

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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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