Oct 2024

RYO KAMOI
ryokamoi@psu.edu
https://ryokamoi.github.io/

RESEARCH INTERESTSMy research interests lie in the broad areas of natural language processing, including trustworthy NLP systems, detecting
and mitigating errors in responses from LLMs, evaluating LLMs, reasoning capabilities of LLMs, fact verification, text
summarization, and vision-language models.

EDUCATION
Pennsylvania State University - Ph.D. Student in Computer Science
Advisor: Dr. Rui Zhang

Aug 2023 - (May 2028)

University of Texas at Austin - Master of Science in Computer Science
Advisor: Dr. Greg Durrett, Mentor: Tanya Goyal

Aug 2020 - Dec 2022

Keio University, Japan - Bachelor of Engineering in Statistics
Apr 2016 - Mar 2020
Advisor: Dr. Kei Kobayashi, Top student in the Department of Mathematics (Keio Engineering Foundation Award)

RESEARCH INTERNSHIPS
Amazon, Cambridge, UK - Applied Scientist Intern- Developed an interpretable answer quality evaluation framework for responses from chatbots.

SELECTED PAPERS

Jul - Dec 2021

https://scholar.google.com/citations?user=4OWTLKAAAAAJ

Ryo Kamoi, Yusen Zhang, Nan Zhang, Jiawei Han, Rui Zhang. 2024. When Can LLMs Actually Correct Their Own
Mistakes? A Critical Survey of Self-Correction of LLMs. TACL (to appear).
Ryo Kamoi, Sarkar Snigdha Sarathi Das, Renze Lou, Jihyun Janice Ahn, Yilun Zhao, Xiaoxin Lu, Nan Zhang, YusenZhang, Ranran Haoran Zhang, Sujeeth Reddy Vummanthala, Salika Dave, Shaobo Qin, Arman Cohan, Wenpeng
Yin, Rui Zhang. 2024. Evaluating LLMs at Detecting Errors in LLM Responses. In COLM.
Ryo Kamoi, Tanya Goyal, Juan Diego Rodriguez, and Greg Durrett. 2023. WiCE: Real-world Entailment for Claims inWikipedia. In EMNLP (main).
Ryo Kamoi, Tanya Goyal, Juan Diego Rodriguez, and Greg Durrett. 2023. Shortcomings of Question Answering Based
Factuality Frameworks for Error Localization. In EACL (main).
Ryo Kamoi and Kei Kobayashi. 2020. Why is the Mahalanobis Distance Effective for Anomaly Detection? arXiv preprint
arXiv:2003.00402.

SERVICES
Reviewer - ARR (2023 - present), COLM (2024), AISTATS (2021), BMVC (2020)
Teaching Assistant, Penn State University
CMPSC 442: Artificial Intelligence
CMPSC 448: Machine Learning and AI

Spring 2024
Fall 2023, Fall 2024

HONORS AND AWARDS
Keio University Global Fellowship - Scholarships for graduate study at UT Austin

2020

Keio Engineering Foundation Award - Graduation with highest honors (First place in the Dept. of Mathematics)

2020

Japan Student Services Organization (JASSO) Exchange Student Scholarship

2018

INVITED TALKSNLP Colloquium https://nlp-colloquium-jp.github.io/

2023

Talk about my research in detecting mistakes in LLM responses
Nagoya NLP Seminar at Nagoya University https://sites.google.com/site/nagoyanlpseminar/

2023

Talk about my research in fact-verification and textual entailment by language models

SKILLS
Programming Languages: Python
Languages: English, Japanese

OTHER PAPERS
Jihyun Janice Ahn, Ryo Kamoi, Lu Cheng, Rui Zhang, Wenpeng Yin. 2024. Direct-Inverse Prompting: Analyzing LLMs'
Discriminative Capacity in Self-Improving Generation. arXiv preprint arXiv:2407.11017.
Yilun Zhao, Yitao Long, Hongjun Liu, Linyong Nan, Lyuhao Chen, Ryo Kamoi, Yixin Liu, Xiangru Tang, Rui Zhang,Arman Cohan. 2023. DocMath-Eval: Evaluating Numerical Reasoning Capabilities of LLMs in Understanding Long
Documents with Tabular Data. In ACL 2024.
Yusen Zhang, Nan Zhang, Yixin Liu, Alexander Fabbri, Junru Liu, Ryo Kamoi, Xiaoxin Lu, Caiming Xiong, Jieyu Zhao,
Dragomir Radev, Kathleen McKeown, Rui Zhang. 2024. Fair Abstractive Summarization of Diverse Perspectives. In
NAACL 2024.
Ryo Kamoi and Kaname Tomite. 2021. Efficient Unknown Object Detection with Discrepancy Networks for SemanticSegmentation. In the NeurIPS 2021 Workshop on Machine Learning for Autonomous Driving.
Ryo Kamoi and Kei Kobayashi. 2020. Out-of-Distribution Detection with Likelihoods Assigned by Deep Generative
Models Using Multimodal Prior Distributions. In The AAAI's Workshop on Artificial Intelligence Safety.
Ryo Kamoi, Kei Kobayashi. 2019. Likelihood Assignment for Out-of-Distribution Inputs in Deep Generative Models isSensitive to Prior Distribution Choice. arXiv preprint arXiv:1911.06515.