Machine unlearning (MU) is gaining increasing attention due to the need to remove or
modify predictions made by machine learning (ML) models. While training models have …

This paper reexamines the research on out-of-distribution (OOD) robustness in the field of
NLP. We find that the distribution shift settings in previous studies commonly lack adequate …

Large neural language models have transformed modern natural language processing
(NLP) applications. However, fine-tuning such models for specific tasks remains challenging …

The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …

Most of today's AI systems focus on using self-attention mechanisms and transformer
architectures on large amounts of diverse data to achieve impressive performance gains. In …

We present an efficient method of pretraining large-scale autoencoding language models
using training signals generated by an auxiliary model. Originated in ELECTRA, this training …

To date, most of recent work under the retrieval-reader framework for open-domain QA
focuses on either extractive or generative reader exclusively. In this paper, we study a hybrid …

Advances in large language models (LLMs) have revolutionized the natural language
processing field. However, the text generated by LLMs can result in various issues, such as …

Identifying relevant knowledge to be used in conversational systems that are grounded in
long documents is critical to effective response generation. We introduce a knowledge …

Despite remarkable advancements in mitigating hallucinations in large language models
(LLMs) by retrieval augmentation, it remains challenging to measure the reliability of LLMs …