no code implementations • 2 Feb 2024 • Tianqi Liu, Zhen Qin, Junru Wu, Jiaming Shen, Misha Khalman, Rishabh Joshi, Yao Zhao, Mohammad Saleh, Simon Baumgartner, Jialu Liu, Peter J. Liu, Xuanhui Wang
In this work, we formulate the LM alignment as a listwise ranking problem and describe the Listwise Preference Optimization (LiPO) framework, where the policy can potentially learn more effectively from a ranked list of plausible responses given the prompt.
no code implementations • 12 Nov 2023 • Vasilisa Bashlovkina, Zhaobin Kuang, Riley Matthews, Edward Clifford, Yennie Jun, William W. Cohen, Simon Baumgartner
Large language models (LLMs) are trained on web-scale corpora that inevitably include contradictory factual information from sources of varying reliability.
no code implementations • 30 Jun 2023 • Vasilisa Bashlovkina, Riley Matthews, Zhaobin Kuang, Simon Baumgartner, Michael Bendersky
We study the ability of transformer-based language models (LMs) to understand social media language.
no code implementations • 21 Dec 2022 • Xiang Deng, Vasilisa Bashlovkina, Feng Han, Simon Baumgartner, Michael Bendersky
Market sentiment analysis on social media content requires knowledge of both financial markets and social media jargon, which makes it a challenging task for human raters.
2 code implementations • ICLR 2022 • Yi Tay, Vinh Q. Tran, Sebastian Ruder, Jai Gupta, Hyung Won Chung, Dara Bahri, Zhen Qin, Simon Baumgartner, Cong Yu, Donald Metzler
In this paper, we propose a new model inductive bias that learns a subword tokenization end-to-end as part of the model.
Ranked #3 on Paraphrase Identification on Quora Question Pairs
no code implementations • WS 2020 • Anjalie Field, Sascha Rothe, Simon Baumgartner, Cong Yu, Abe Ittycheriah
We evaluate the performance of transformer encoders with various decoders for information organization through a new task: generation of section headings for Wikipedia articles.