no code implementations • 14 Dec 2023 • Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, Balaji Lakshminarayanan
Safe deployment of large language models (LLMs) may benefit from a reliable method for assessing their generated content to determine when to abstain or to selectively generate.
1 code implementation • 5 Oct 2023 • Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, Thang Luong
Specifically, we introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types, including questions that require fast-changing world knowledge as well as questions with false premises that need to be debunked.
no code implementations • 24 May 2023 • Sheng Shen, Le Hou, Yanqi Zhou, Nan Du, Shayne Longpre, Jason Wei, Hyung Won Chung, Barret Zoph, William Fedus, Xinyun Chen, Tu Vu, Yuexin Wu, Wuyang Chen, Albert Webson, Yunxuan Li, Vincent Zhao, Hongkun Yu, Kurt Keutzer, Trevor Darrell, Denny Zhou
Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnable parameters to Large Language Models (LLMs) without increasing inference cost.
1 code implementation • 1 May 2023 • Tu Vu, Van Thong Huynh, Soo-Hyung Kim
This paper presents an efficient Multi-scale Transformer-based approach for the task of Emotion recognition from Physiological data, which has gained widespread attention in the research community due to the vast amount of information that can be extracted from these signals using modern sensors and machine learning techniques.
no code implementations • 16 Mar 2023 • Tu Vu, Van Thong Huynh, Soo Hyung Kim
Facial Action Units detection (FAUs) represents a fine-grained classification problem that involves identifying different units on the human face, as defined by the Facial Action Coding System.
1 code implementation • 31 Jan 2023 • Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V. Le, Barret Zoph, Jason Wei, Adam Roberts
We study the design decisions of publicly available instruction tuning methods, and break down the development of Flan 2022 (Chung et al., 2022).
1 code implementation • 25 Jan 2023 • Cong Dao Tran, Nhut Huy Pham, Anh Nguyen, Truong Son Hy, Tu Vu
This paper presents ViDeBERTa, a new pre-trained monolingual language model for Vietnamese, with three versions - ViDeBERTa_xsmall, ViDeBERTa_base, and ViDeBERTa_large, which are pre-trained on a large-scale corpus of high-quality and diverse Vietnamese texts using DeBERTa architecture.
no code implementations • 2 Nov 2022 • Jiao Sun, Thibault Sellam, Elizabeth Clark, Tu Vu, Timothy Dozat, Dan Garrette, Aditya Siddhant, Jacob Eisenstein, Sebastian Gehrmann
Evaluation metrics that are not robust to dialect variation make it impossible to tell how well systems perform for many groups of users, and can even penalize systems for producing text in lower-resource dialects.
1 code implementation • 25 May 2022 • Tu Vu, Aditya Barua, Brian Lester, Daniel Cer, Mohit Iyyer, Noah Constant
In this paper, we explore the challenging problem of performing a generative task in a target language when labeled data is only available in English, using summarization as a case study.
2 code implementations • 25 May 2022 • Dheeraj Mekala, Tu Vu, Timo Schick, Jingbo Shang
The ability of generative language models (GLMs) to generate text has improved considerably in the last few years, enabling their use for generative data augmentation.
no code implementations • ACL 2022 • Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou, Daniel Cer
Finally, we propose an efficient retrieval approach that interprets task prompts as task embeddings to identify similar tasks and predict the most transferable source tasks for a novel target task.
1 code implementation • EMNLP 2021 • Tu Vu, Minh-Thang Luong, Quoc V. Le, Grady Simon, Mohit Iyyer
Despite their recent successes in tackling many NLP tasks, large-scale pre-trained language models do not perform as well in few-shot settings where only a handful of training examples are available.
Ranked #1 on Few-Shot NLI on SNLI (8 training examples per class)
1 code implementation • EMNLP 2020 • Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, Mohit Iyyer
We also develop task embeddings that can be used to predict the most transferable source tasks for a given target task, and we validate their effectiveness in experiments controlled for source and target data size.
1 code implementation • ACL 2019 • Tu Vu, Mohit Iyyer
While paragraph embedding models are remarkably effective for downstream classification tasks, what they learn and encode into a single vector remains opaque.
no code implementations • SEMEVAL 2018 • Tu Vu, Vered Shwartz
Supervised distributional methods are applied successfully in lexical entailment, but recent work questioned whether these methods actually learn a relation between two words.
no code implementations • NAACL 2018 • Tu Vu, Baotian Hu, Tsendsuren Munkhdalai, Hong Yu
Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications.
Ranked #1 on Text Simplification on PWKP / WikiSmall