Self-adaptive LLM dynamically adjusts its weights to learn new tasks
A trio of AI researchers at Sakana AI, a Japanese startup, has announced the development of a self-adaptive AI LLM called Transformer2. Qi Sun, Edoardo Cetin, and Yujin Tang, have posted their paper on the arXiv preprint server.
This paper was accepted at the Efficient Natural Language and Speech Processing (ENLSP) Workshop at NeurIPS 2024. Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recent advancements in mixture of expert (MoE) models, speculative…
Recent advances in large language models (LLMs) test-time computing have introduced the capability to perform intermediate chain-of-thought (CoT) reasoning (thinking) before generating answers. While increasing the thinking budget yields smooth performance improvements at inference time, the relationship between LLM capability, query complexity, and optimal budget allocation remains poorly understood for…
Reasoning large language models (LLMs) are designed to solve complex problems by breaking them down into a series of smaller steps. These powerful models are particularly good at challenging tasks like advanced programming and multistep planning. But developing reasoning models demands an enormous amount of computation and energy due to…