Whispering Llama
Research on "Whispering Llama," a metaphorical term encompassing various advancements in Large Language Models (LLMs) based on the Llama architecture, focuses on improving their capabilities and addressing limitations. Current efforts concentrate on enhancing reasoning abilities through techniques like Monte Carlo Tree Search and pairwise reward models, improving linguistic plausibility via phoneme-based tokenization, and mitigating safety risks by developing robust safeguards against adversarial attacks and biases. These advancements are significant because they contribute to creating more efficient, reliable, and ethically sound LLMs with broader applications in diverse fields, from mathematical problem-solving to improved speech recognition.
Papers
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference
Parsa Kavehzadeh, Mojtaba Valipour, Marzieh Tahaei, Ali Ghodsi, Boxing Chen, Mehdi Rezagholizadeh
Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca
Pinzhen Chen, Shaoxiong Ji, Nikolay Bogoychev, Andrey Kutuzov, Barry Haddow, Kenneth Heafield