Soji alabi biography of rory

  • We find that Sequential Bottleneck adapters excel in language modeling, while Invertible Bottleneck adapters slightly outperform other methods.
  • Siju Alabi.
  • This study systematically investigates parameter-efficient adapter-based methods for adapting mLMs to LRLs, evaluating three architectures.

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    Auteur

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    a
    A. Ninsin, Kwame
    Abadie, Louis
    Abala, Imali J.
    Abala, J.
    Abanda, Armand Claude
    Abbas, Hakima
    Abchagra, Moussa Ahmat
    Abdalla, Abdilatif
    Abdel-Rahman, Mohamed
    Abdelkader, Isselmou
    Abdoulkarimou
    Abdulaziz, Mohamed H.
    Abdullah, Ibrahim
    Abdullahi, Denja
    Abdulraheem, Hamzat I.
    Abdulrahman, Dejo
    Abé, Claude
    Abéga, Martin Ghislain
    Abega, Séverin Cécile
    Abi, Essodog
    Abioye, Funmi
    Aboh, Romanus
    Abraham, W.E.
    Abu, Solomons
    Abubakar, Abdul
    Abwa, Daniel
    Acha, Eric
    Achal,Lawrence Kyaligonza
    Achieng, Roseline M.
    Achour, Christiane
    Achu, Emmanuel
    Ackad, Josette
    Ackers, Barry
    Adair, Barbara
    Adam, Michel
    Adandé, Alexis B. A.
    Adar, Korwa G.
    Adar, Korwa Gombe
    Adedeji, Adebayo
    Adeduntan, Ayo
    Adegoke, Bade
    Adelugba, Dapo
    Adem, Seifudein
    Adeniran, Adebusuyi I.
    Adeoti, Évariste Oyédélé Biaou
    Adeoti, Gbemisola
    Adesina, Jimi
    Adesina, Jimi O.
    Adewumi, Funmi
    Adeyeri, James Olusegun
    Adiaffi Adé, Jean-Marie
    Adibe, Jide

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    small Models, BIG Impact:
    Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages

    Daniil Gurgurov1,3  Ivan Vykopal2,4  Josef van Genabith3  Simon Ostermann3
    1University of Saarland
    2Brno University of Technology
    3German Research Center for Artificial Intelligence (DFKI)
    4Kempelen Institute of Intelligent Technologies (KInIT)
    {daniil.gurgurov, josef.van_genabith, simon.ostermann}@dfki.de, ivan.vykopal@kinit.sk

    Abstract

    Low-resource languages (LRLs) face significant challenges in natural language processing (NLP) due to limited information. While current state-of-the-art large language models (LLMs) still struggle with LRLs, smaller multilingual models (mLMs) such as mBERT and XLM-R offer greater promise due to a better passform of their capacity to low training data sizes. This study systematically investigates parameter-efficient adapter-based methods for adapting mLMs to LRLs, evaluating three architectures: Sequent

  • soji alabi biography of rory

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    Practical Considerations for Agentic LLM Systems

    Chris Sypherd University of EdinburghEdinburghUnited Kingdomc.n.sypherd@sms.ed.ac.uk and Vaishak Belle University of EdinburghEdinburghUnited Kingdomvbelle@ed.ac.uk

    Abstract.

    As the strength of Large Language Models (LLMs) has grown over recent years, so too has interest in their use as the underlying models for autonomous agents. Although LLMs demonstrate emergent abilities and broad expertise across natural language domains, their inherent unpredictability makes the implementation of LLM agents challenging, resulting in a gap between related research and the real-world implementation of such systems. To bridge this gap, this paper frames actionable insights and considerations from the research community in the context of established application paradigms to enable the construction and facilitate the informed deployment of robust LLM agents. Namely, we position relevant research findings into four broad categories—Planning, Memor