168 citations · 471 across the 17 of their papers we have counts for
18 papers
FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
Xinyi Wang, John Wieting, Jonathan H. Clark
Learning paradigms for large language models (LLMs) currently tend to fall within either in-context learning (ICL) or full fine-tuning. Each of these comes with their own trade-off…
The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation
Patrick Fernandes, Daniel Deutsch, Mara Finkelstein +7
Automatic evaluation of machine translation (MT) is a critical tool driving the rapid iterative development of MT systems. While considerable progress has been made on estimating a…
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
Sebastian Ruder, Jonathan H. Clark, Alexander Gutkin +24
Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) -- languages for which NLP re-search is part…
AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages
Odunayo Ogundepo, Tajuddeen R. Gwadabe, Clara E. Rivera +49
African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingu…
Evaluating and Modeling Attribution for Cross-Lingual Question Answering
Benjamin Muller, John Wieting, Jonathan H. Clark +6
Trustworthy answer content is abundant in many high-resource languages and is instantly accessible through question answering systems, yet this content can be hard to access for th…
PaLM 2 Technical Report
Rohan Anil, Andrew M. Dai, Orhan Firat +125
We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 i…
Fundamental Symmetries, Neutrons, and Neutrinos (FSNN): Whitepaper for the 2023 NSAC Long Range Plan
B. Acharya, C. Adams, A. A. Aleksandrova +176
This whitepaper presents the research priorities decided on by attendees of the 2022 Town Meeting for Fundamental Symmetries, Neutrons and Neutrinos, which took place December 13-1…
Nuclear decay as a probe for physics beyond the Standard Model
M. Brodeur, N. Buzinsky, M. A. Caprio +43
This white paper was submitted to the 2022 Fundamental Symmetries, Neutrons, and Neutrinos (FSNN) Town Hall Meeting in preparation for the next NSAC Long Range Plan. We advocate to…
Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval
John Wieting, Jonathan H. Clark, William W. Cohen +2
Contrastive learning has been successfully used for retrieval of semantically aligned sentences, but it often requires large batch sizes or careful engineering to work well. In thi…
MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages
Akari Asai, Shayne Longpre, Jungo Kasai +6
We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typo…
XTREME-S: Evaluating Cross-lingual Speech Representations
Alexis Conneau, Ankur Bapna, Yu Zhang +16
We introduce XTREME-S, a new benchmark to evaluate universal cross-lingual speech representations in many languages. XTREME-S covers four task families: speech recognition, classif…
Scaling Up Models and Data with and
Adam Roberts, Hyung Won Chung, Anselm Levskaya +40
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can…
SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training
Ankur Bapna, Yu-an Chung, Nan Wu +7
Unsupervised pre-training is now the predominant approach for both text and speech understanding. Self-attention models pre-trained on large amounts of unannotated data have been h…
CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation
Jonathan H. Clark, Dan Garrette, Iulia Turc +1
Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step. While recent toke…
CapWAP: Captioning with a Purpose
Adam Fisch, Kenton Lee, Ming-Wei Chang +2
The traditional image captioning task uses generic reference captions to provide textual information about images. Different user populations, however, will care about different vi…
Learning to Recognize Dialect Features
Dorottya Demszky, Devyani Sharma, Jonathan H. Clark +2
Building NLP systems that serve everyone requires accounting for dialect differences. But dialects are not monolithic entities: rather, distinctions between and within dialects are…
XOR QA: Cross-lingual Open-Retrieval Question Answering
Akari Asai, Jungo Kasai, Jonathan H. Clark +3
Multilingual question answering tasks typically assume answers exist in the same language as the question. Yet in practice, many languages face both information scarcity -- where l…
TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages
Jonathan H. Clark, Eunsol Choi, Michael Collins +4
Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. We present TyDi QA---a question answering dataset covering 11 typologically dive…