1 code implementation • 3 Oct 2023 • Jieyu Zhang, Ranjay Krishna, Ahmed H. Awadallah, Chi Wang
Today, users ask Large language models (LLMs) as assistants to answer queries that require external knowledge; they ask about the weather in a specific city, about stock prices, and even about where specific locations are within their neighborhood.
4 code implementations • 8 Mar 2023 • Chi Wang, Susan Xueqing Liu, Ahmed H. Awadallah
Large Language Models (LLMs) have sparked significant interest in their generative capabilities, leading to the development of various commercial applications.
1 code implementation • 20 Dec 2022 • Yixin Liu, Budhaditya Deb, Milagro Teruel, Aaron Halfaker, Dragomir Radev, Ahmed H. Awadallah
We collect a high-quality dataset, DeFacto, containing human demonstrations and informational natural language feedback consisting of corrective instructions, edited summaries, and explanations with respect to the factual consistency of the summary.
1 code implementation • 25 May 2022 • Yixin Liu, Ansong Ni, Linyong Nan, Budhaditya Deb, Chenguang Zhu, Ahmed H. Awadallah, Dragomir Radev
Our experimental results show that our model has a better performance compared with strong baselines with efficient attention modules, and our analysis provides further insights into our locality-aware modeling strategy.
no code implementations • 16 Apr 2022 • Shashank Gupta, Subhabrata Mukherjee, Krishan Subudhi, Eduardo Gonzalez, Damien Jose, Ahmed H. Awadallah, Jianfeng Gao
Traditional multi-task learning (MTL) methods use dense networks that use the same set of shared weights across several different tasks.
2 code implementations • ACL 2022 • Yusen Zhang, Ansong Ni, Ziming Mao, Chen Henry Wu, Chenguang Zhu, Budhaditya Deb, Ahmed H. Awadallah, Dragomir Radev, Rui Zhang
To the best of our knowledge, Summ$^N$ is the first multi-stage split-then-summarize framework for long input summarization.
1 code implementation • ACL 2022 • Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed H. Awadallah, Dragomir Radev
Transformer-based models have achieved state-of-the-art performance on short-input summarization.