Open Source Large Language Model
Open-source large language models (LLMs) aim to provide accessible and customizable alternatives to proprietary models, fostering research and development while addressing concerns about data privacy and vendor lock-in. Current research focuses on adapting these models to specific languages and domains (e.g., Romanian, medicine, finance), improving their reasoning capabilities through techniques like retrieval-augmented generation and mixture-of-experts architectures, and optimizing their deployment efficiency on various hardware. This burgeoning field significantly impacts both the scientific community, by enabling broader participation in LLM research, and practical applications, offering cost-effective and adaptable solutions for diverse tasks ranging from question answering to code generation.
Papers
Evaluating In-Context Learning of Libraries for Code Generation
Arkil Patel, Siva Reddy, Dzmitry Bahdanau, Pradeep Dasigi
Bergeron: Combating Adversarial Attacks through a Conscience-Based Alignment Framework
Matthew Pisano, Peter Ly, Abraham Sanders, Bingsheng Yao, Dakuo Wang, Tomek Strzalkowski, Mei Si