The Language Understanding and Knowledge Acquisition (LUKA) Lab is directed by Dr. Muhao Chen. Our research focuses on robust and accountable machine learning methods for natural language understanding and knowledge acquisition from unstructured data. Most recently, we have been focusing on accountability and security problems with large language models. Our long-term goal is to develop robust, generalizable and trustworthy learning systems that help machines understand nature.
We will present 9 papers at EMNLP 2023 (incl. 4 main and 5 findings), as well as 1 paper at CoNLL 2023.
We will give a tutorial on "Combating Security and Privacy Issues in the Era of Large Language Models" at NAACL 2024 and another tutorial on "Enhancing LLM Capabilities Beyond Scaling Up" at EMNLP 2024.
We are delighted to receive the Amazon Research Award on Generative AI.
We have received the NSF Proto-OKN grant that will support our research on knowledge acquisition.
We have 7 papers accepted to ACL 2023. Congratulations to all the authors, especially to Tanay and Shudi who are undergraduate lead-authors to two of those.
Congratulations to our first two PhD graduates, Ehsan and Wenxuan, for passing their dissertation defense.
We will give a tutorial about "Indirectly Supervised Natural Language Processing" at ACL 2023.
We are delighted to receive the Amazon Research Award on Responsible AI.
We have 6 long papers accepted to EMNLP 2022. (Also check out our recent AACL papers)
We have received a Cisco Faculty Award to support our research on robust knowledge extraction.)
We will present 7 long papers at NAACL 2022 (incl. 4 main, 2 findings and 1 TACL). See you in Seattle. (Also check out our recent ACL and ICLR papers)
We have a new paper on indirect supervision accepted to TACL. Congratulations to the great work by our undergraduate scholar Bangzheng.
We will give a tutorial about "New Frontiers in Information Extraction" at NAACL 2022.
We have 6 long papers accepted to EMNLP 2021. Also check out our new ACL papers.
We have received the NSF CRII Award that will support our research on transferable representation learning.
