Large language models have achieved impressive performance on various natural
language processing tasks. However, so far they have been evaluated primarily on …

We present Branch-Train-Merge (BTM), a communication-efficient algorithm for
embarrassingly parallel training of large language models (LLMs). We show it is possible to …

Reading strategies have been shown to improve comprehension levels, especially for
readers lacking adequate prior knowledge. Just as the process of knowledge accumulation …

We propose EXAMS--a new benchmark dataset for cross-lingual and multilingual question
answering for high school examinations. We collected more than 24,000 high-quality high …

Although neural network approaches achieve remarkable success on a variety of NLP tasks,
many of them struggle to answer questions that require commonsense knowledge. We …

Code generation aims to automatically generate code snippets of specific programming
language according to natural language descriptions. The continuous advancements in …

Open book question answering is a type of natural language based QA (NLQA) where
questions are expected to be answered with respect to a given set of open book facts, and …

Our goal is a teachable reasoning system for question-answering (QA), where a user can
interact with faithful answer explanations, and correct its errors so that the system improves …

We present the ARC-DA dataset, a direct-answer (" open response"," freeform") version of
the ARC (AI2 Reasoning Challenge) multiple-choice dataset. While ARC has been …

We focus on multiple-choice question answering (QA) tasks in subject areas such as
science, where we require both broad background knowledge and the facts from the given …