Natural language processing (NLP) has significantly transformed in the last decade,
especially in the field of language modeling. Large language models (LLMs) have achieved …

Optimizing large language models (LLMs) for downstream use cases often involves the
customization of pre-trained LLMs through further fine-tuning. Meta's open release of Llama …

Large language models (LLMs) excel at few-shot in-context learning (ICL)--learning from a
few examples provided in context at inference, without any weight updates. Newly expanded …

With recent advancements in natural language processing, Large Language Models (LLMs)
have emerged as powerful tools for various real-world applications. Despite their prowess …

Large language models (LLMs) need knowledge updates to meet the ever-growing world
facts and correct the hallucinated responses, facilitating the methods of lifelong model …

As model context lengths continue to increase, the number of demonstrations that can be
provided in-context approaches the size of entire training datasets. We study the behavior of …

In this work, we evaluate 10 open-source instructed LLMs on four representative code
comprehension and generation tasks. We have the following main findings. First, for the zero …

To determine the safety of large language models (LLMs), AI developers must be able to
assess their dangerous capabilities. But simple prompting strategies often fail to elicit an …

Logs are important in modern software development with runtime information. Log parsing is
the first step in many log-based analyses, that involve extracting structured information from …

While Large Language Models (LLMs) have demonstrated exceptional multitasking abilities,
fine-tuning these models on downstream, domain-specific datasets is often necessary to …