Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …

Abstract Knowledge-intensive language tasks require NLP systems to both provide the
correct answer and retrieve supporting evidence for it in a given corpus. Autoregressive …

The factual knowledge acquired during pre-training and stored in the parameters of
Language Models (LMs) can be useful in downstream tasks (eg, question answering or …

Current state-of-the-art document retrieval solutions mainly follow an index-retrieve
paradigm, where the index is hard to be directly optimized for the final retrieval target. In this …

Large-scale multi-modal pre-training models such as CLIP and PaLI exhibit strong
generalization on various visual domains and tasks. However, existing image classification …

We present a study on leveraging multilingual pre-trained generative language models for
zero-shot cross-lingual event argument extraction (EAE). By formulating EAE as a language …

Structured and grounded representation of text is typically formalized by closed information
extraction, the problem of extracting an exhaustive set of (subject, relation, object) triplets …

Abstract We present Cross-lingual Open-Retrieval Answer Generation (CORA), the first
unified many-to-many question answering (QA) model that can answer questions across …

The field of Natural Language Processing (NLP) has experienced significant growth in
recent years, largely due to advancements in Deep Learning technology and especially …

The Differentiable Search Index (DSI) is an emerging paradigm for information retrieval.
Unlike traditional retrieval architectures where index and retrieval are two different and …