This work elicits LLMs' inherent ability to handle long contexts without fine-tuning. The
limited length of the training sequence during training may limit the application of Large …

Long context capability is a crucial competency for large language models (LLMs) as it
mitigates the human struggle to digest long-form texts. This capability enables complex task …

Recent work has shown that large language models, or LLMs, are capable of amazing
processing context windows based on the nuance and complexity of respective input …

Text embedding has become a foundational technology in natural language processing
(NLP) during the deep learning era, driving advancements across a wide array of …

Large language models (LLMs) have achieved significant performance gains using
advanced prompting techniques over various tasks. However, the increasing length of …

The increasing prevalence of large language models (LLMs) such as GPT-4 in various
applications has led to a surge in the size of prompts required for optimal performance …

Embedding models are crucial for various natural language processing tasks but can be
limited by factors such as limited vocabulary, lack of context, and grammatical errors. This …

Leveraging large language models (LLMs) for complex natural language tasks typically
requires long-form prompts to convey detailed requirements and information, which results …

Large language models have revolutionized data processing in numerous domains, with
their ability to handle extended context reasoning receiving notable recognition. To speed …

Offering rich contexts to Large Language Models (LLMs) has shown to boost the
performance in various tasks, but the resulting longer prompt would increase the …