Yichen Jiang

Yichen Jiang (姜翌辰）

I am a Research Scientist at Apple AI/ML. I am interested in LLM's Chain-of-Thought/multi-hop reasoning and knowledge storage/extraction. I got my PhD in May 2024 from Department of Computer Science at University of North Carolina at Chapel Hill, advised by Prof. Mohit Bansal from in UNC-NLP Research Group. My PhD thesis focuses on analyzing and improving neural networks' ability to understand the compositional structures underlying natural language, as well as interpretability and adversarial robustness of multi-hop reasoning. Before PhD, I finished my BS + MS at UNC Chapel Hill and interned at Microsoft Research and Facebook AI. I'm supported by the Apple Scholars in AI/ML PhD fellowship.

Email / CV / Github / Google Scholar / LinkedIn

Research

My research focuses on analyzing and improving the neural network’s ability to understand the compositional structures underlying natural language sentences. In the past, I showed how existing models lack compositionality and take reasoning shortcuts. I then designed interpretable and modular models that can answer complex multi-hop questions more robustly and also collected a multi-hop fact verification dataset HoVer to motivate future work. I also incorporated Tensor-Product into a Transformer for better abstractive summarization. My ultimate goal is to build AI systems that can compositionally recombine structures and contents in understanding natural language and comprehending this world.

	Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings Yichen Jiang, Xiang Zhou, and Mohit Bansal, Proceedings of ACL 2024 arxiv / code
	Hierarchical and Dynamic Prompt Compression for Efficient Zero-shot API Usage Yichen Jiang, Marco Del Vecchio, Mohit Bansal, and Anders Johannsen Findings of EACL 2024
	Data Factors for Better Compositional Generalization Xiang Zhou, Yichen Jiang, and Mohit Bansal Proceedings of EMNLP 2023 arxiv / code / bibtex
	Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality Yichen Jiang, Xiang Zhou, and Mohit Bansal Proceedings of EMNLP 2022 arxiv / code / bibtex
	Inducing Transformer's Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks Yichen Jiang and Mohit Bansal Proceedings of EMNLP 2021 arxiv / code / bibtex
	Learning and Analyzing Generation Order for Undirected Sequence Models Yichen Jiang and Mohit Bansal Findings of EMNLP 2021 arxiv / code / bibtex
	Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages Paul Soulos, Sudha Rao, Caitlin Smith, Eric Rosen, Asli Celikyilmaz, R. Thomas McCoy, Yichen Jiang, Coleman Haley, Roland Fernandez, Hamid Palangi, Jianfeng Gao, Paul Smolensky Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021) paper / bibtex
	Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization Yichen Jiang, Asli Celikyilmaz, Paul Smolensky, Paul Soulus, Sudha Rao, Hamid Palangi, Roland Fernandez, Caitlin Smith, Mohit Bansal, Jianfeng Gao Proceedings of NAACL-HLT 2021 arxiv / code / bibtex
	HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification Yichen Jiang, Shikha Bordia, Zheng Zhong, Charles Dognin, Maneesh Singh, Mohit Bansal Findings of EMNLP 2020 arxiv / data+code / bibtex
	Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning Yichen Jiang, Mohit Bansal Proceedings of EMNLP 2019, Hong Kong, China arxiv / code / bibtex
	Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA Yichen Jiang, Mohit Bansal Proceedings of ACL 2019, Florence, Italy arxiv / code / slides / bibtex
	Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension Yichen Jiang, Nitish Joshi, Yen-chun Chen, and Mohit Bansal Proceedings of ACL 2019, Florence, Italy arxiv / code / slides / bibtex
	Closed-book Training to Improve Summarization Encoder Memory Yichen Jiang, Mohit Bansal Proceedings of EMNLP 2018, Brussels, Belgium arxiv / poster / bibtex

Work/Intern Experience
Apple AIML	2023 May - 2023 October	Supervised by Marco Del Vecchio and Dr. Anders Johannsen.
Amazon Alexa AI	2022 May - 2022 November	Supervised by Dr. Di Jin, Dr. Mahdi Namazifar, Dr. Yang Liu, and Dr. Dilek Hakkani-tur.
Facebook AI	2021 May - 2021 August	Supervised by Dr. Barlas Oguz, Dr. Scott Yih, and Dr. Yashar Mehdad.
Microsoft Research, Redmond	2020 June - 2020 August	Supervised by Dr. Asli Celikyilmaz and Prof. Paul Smolensky.
Verisk Analytics	2019 May - 2019 August	Supervised by Dr. Maneesh Singh

Thanks for providing the template source code for this website