Machine Reading Comprehension (MRC) is a challenging task and hot topic in Natural
Language Processing. The goal of this field is to develop systems for answering the …

Large language models (LLMs) show impressive abilities via few-shot prompting.
Commercialized APIs such as OpenAI GPT-3 further increase their use in real-world …

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 …

Prompts for pre-trained language models (PLMs) have shown remarkable performance by
bridging the gap between pre-training tasks and various downstream tasks. Among these …

There has been growing interest in parameter-efficient methods to apply pre-trained
language models to downstream tasks. Building on the Prompt Tuning approach of Lester et …

Question answering (QA) tasks have been posed using a variety of formats, such as
extractive span selection, multiple choice, etc. This has led to format-specialized models …

While pretrained models such as BERT have shown large gains across natural language
understanding tasks, their performance can be improved by further training the model on a …

We present the results of the Machine Reading for Question Answering (MRQA) 2019
shared task on evaluating the generalization capabilities of reading comprehension …

This work introduces a new multi-task, parameter-efficient language model (LM) tuning
method that learns to transfer knowledge across different tasks via a mixture of soft prompts …

Bidirectional Encoder Representations from Transformers (BERT) and BERT-based
approaches are the current state-of-the-art in many natural language processing (NLP) …