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 …

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

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

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 …

Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-
learning or few-shot learning, aims to effectively train a model using only a small amount of …

Query rewriting plays a vital role in enhancing conversational search by transforming context-
dependent user queries into standalone forms. Existing approaches primarily leverage …

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 …

Finding the best way of adapting pre-trained language models to a task is a big challenge in
current NLP. Just like the previous generation of task-tuned models (TT), models that are …

Prompting is a standard tool for customizing language-model chatbots, enabling them to
take on a specific" persona". An implicit assumption in the use of prompts is that they will be …

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 …