The field of large language models is moving towards more efficient and innovative architectures. Researchers are exploring new methods to reduce the computational cost and energy demand of these models, while maintaining their performance. One direction is the use of reservoir computing, which has shown promising results in terms of efficiency. Another approach is the development of character-level decoding methods, which can reduce the output projection layer's computational cost. Additionally, there is a growing interest in applying quantum computing methods to optimize certain tasks related to large language models. Noteworthy papers include:

• SpeLLM: Character-Level Multi-Head Decoding, which proposes a method to decouple input and output vocabularies, enabling the representation of a larger output space with smaller linear heads.
• Efficient Uncertainty in LLMs through Evidential Knowledge Distillation, which introduces a novel approach to efficient uncertainty estimation in LLMs through evidential distillation.