This paper highlights the importance of personalization in large language models and
introduces the LaMP benchmark--a novel benchmark for training and evaluating language …

Large language models (LLMs) are used to generate content for a wide range of tasks, and
are set to reach a growing audience in coming years due to integration in product interfaces …

This paper studies retrieval-augmented approaches for personalizing large language
models (LLMs), which potentially have a substantial impact on various applications and …

Some tasks in content processing, eg, natural language processing (NLP), like hate or
offensive speech and emotional or funny text detection, are subjective by nature. Each …

Large language models (LLMs) have significantly advanced Natural Language Processing
(NLP) tasks in recent years. However, their universal nature poses limitations in scenarios …

Long-text generation is seemingly ubiquitous in real-world applications of large language
models such as generating an email or writing a review. Despite the fundamental …

The increasing demand for personalized interactions with large language models (LLMs)
calls for methodologies capable of accurately and efficiently identifying user opinions and …

Instead of using a single ground truth for language processing tasks, several recent studies
have examined how to represent and predict the labels of the set of annotators. However …

With the rapid growth of online medical platforms, more and more doctors are willing to
manage and communicate with patients via online services. Considering the large volume …

As research in human-centered NLP advances, there is a growing recognition of the
importance of incorporating human and social factors into NLP models. At the same time …