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

Sentiment analysis is one of the traditional well-known tasks in Natural Language
Processing (NLP) research. In recent years, Pre-trained Models (PMs) have become one of …

Question Answering (QA) is a longstanding challenge in natural language processing.
Existing QA works mostly focus on specific question types, knowledge domains, or …

Global models are trained to be as generalizable as possible, with user invariance
considered desirable since the models are shared across multitudes of users. As such …

Task generalization has been a long-standing challenge in Natural Language Processing
(NLP). Recent research attempts to improve the task generalization ability of pre-trained …

Data Maps is an interesting method of graphical representation of datasets, which allows
observing the model's behaviour for individual instances in the learning process (training …

Effectively and efficiently adapting a pre-trained language model (PLM) for human-centered
text understanding (HCTU) is challenging since user tokens are million-level in most …

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 …

We present a new method LiST is short for Lite Prompted Self-Training for parameter-
efficient fine-tuning of large pre-trained language models (PLMs) for few-shot learning. LiST …

Reusable embeddings of user behaviour have shown significant performance
improvements for the personalised saliency prediction task. However, prior works require …