Search Results for author: Ryo Kamoi

Found 6 papers, 3 papers with code

DocMath-Eval: Evaluating Numerical Reasoning Capabilities of LLMs in Understanding Long Documents with Tabular Data

no code implementations16 Nov 2023 Yilun Zhao, Yitao Long, Hongjun Liu, Linyong Nan, Lyuhao Chen, Ryo Kamoi, Yixin Liu, Xiangru Tang, Rui Zhang, Arman Cohan

This paper introduces DocMath-Eval, a comprehensive benchmark specifically designed to evaluate the numerical reasoning and problem-solving capabilities of LLMs in the context of understanding and analyzing financial documents containing both text and tables.

Math

Fair Abstractive Summarization of Diverse Perspectives

1 code implementation14 Nov 2023 Yusen Zhang, Nan Zhang, Yixin Liu, Alexander Fabbri, Junru Liu, Ryo Kamoi, Xiaoxin Lu, Caiming Xiong, Jieyu Zhao, Dragomir Radev, Kathleen McKeown, Rui Zhang

We first formally define fairness in abstractive summarization as not underrepresenting perspectives of any groups of people and propose four reference-free automatic metrics measuring the differences between target and source perspectives.

Abstractive Text Summarization Fairness

WiCE: Real-World Entailment for Claims in Wikipedia

1 code implementation2 Mar 2023 Ryo Kamoi, Tanya Goyal, Juan Diego Rodriguez, Greg Durrett

Textual entailment models are increasingly applied in settings like fact-checking, presupposition verification in question answering, or summary evaluation.

Fact Checking Natural Language Inference +3

Why is the Mahalanobis Distance Effective for Anomaly Detection?

no code implementations1 Mar 2020 Ryo Kamoi, Kei Kobayashi

This suggests that the reason the Mahalanobis confidence score works so well is mistaken, and makes use of different information from ODIN, another popular OoD detection method based on prediction confidence.

Anomaly Detection General Classification +2

Likelihood Assignment for Out-of-Distribution Inputs in Deep Generative Models is Sensitive to Prior Distribution Choice

no code implementations15 Nov 2019 Ryo Kamoi, Kei Kobayashi

This paper focuses on the relationship between the choice of a prior distribution and the likelihoods assigned to out-of-distribution inputs.

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