The paper examines in-context learning (ICL) in large language models based on the transformer architecture. It explores how transformers learn in complex scenarios, specifically studying learning with representations. The authors…
Continue readingThis paper discusses the challenge of Transformer models’ performance decay on inputs longer than those used during training. It introduces a novel functional relative position encoding with progressive interpolation, FIRE,…
Continue readingThe study delves into the in-context learning capabilities of Transformers and Large Language Models (LLMs). The paper demonstrates that Transformers are capable of implementing gradient-based learning algorithms for various real-valued…
Continue readingLONGNET is designed to address the challenge of scaling sequence length in large language models. Traditional methods struggle with either computational complexity or model expressivity, limiting the maximum sequence length….
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