1 citations · 1 across the 17 of their papers we have counts for
19 papers
How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing
Anuj Kumar, Shintaro Ozaki, Yamuna Prasad +3
Recent advances have significantly improved the performance of Abstract Meaning Representation (AMR) parsers, yet determining when their outputs can be trusted remains largely unex…
Overfitting Mitigation via Singular Value Decomposition in Minimum Bayes Risk Decoding
Riza Setiawan Soetedjo, Yusuke Sakai, Hidetaka Kamigaito +2
Minimum Bayes Risk (MBR) decoding enables high-quality text generation by selecting the hypothesis that maximizes a utility metric over sampled pseudo-references. However, it is hi…
ExpArt-KG: Artwork Image Description Generation through Iterative Exploration of Knowledge Graphs
Yuta Kato, Shintaro Ozaki, Kazuki Hayashi +4
Large Vision-Language Models (LVLMs) achieve strong performance on image-grounded text generation and visual question answering. However, it remains difficult for them to comprehen…
Noisy-Channel Minimum Bayes Risk Decoding
Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
Minimum Bayes Risk (MBR) decoding yields more robust and higher-quality text generation than maximum a posteriori (MAP) decoding by selecting hypotheses that maximize expected util…
Multilinguality of Large Language Models From a Structural Perspective
Haruki Sakajo, Yusuke Sakai, Hidetaka Kamigaito +1
Large language models (LLMs) have excelled in processing multiple languages through pre- and post-training on multilingual data, even though English dominates the training data. Pr…
Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding
Riza Setiawan Soetedjo, Yusuke Sakai, Hidetaka Kamigaito +3
Improving the quality of model-generated summaries, especially factuality, the accuracy of a summary with respect to its source content, remains a challenge. While reranking could…
CArtBench: Evaluating Vision-Language Models on Chinese Art Understanding, Interpretation, and Authenticity
Xuefeng Wei, Zhixuan Wang, Xuan Zhou +5
We introduce CARTBENCH, a museum-grounded benchmark for evaluating vision-language models (VLMs) on Chinese artworks beyond short-form recognition and QA. CARTBENCH comprises four…
HalluCiteChecker: A Lightweight Toolkit for Hallucinated Citation Detection and Verification in the Era of AI Scientists
Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
We introduce HalluCiteChecker, a toolkit for detecting and verifying hallucinated citations in scientific papers. While AI assistant technologies have transformed the academic writ…
Revisiting Non-Verbatim Memorization in Large Language Models: The Role of Entity Surface Forms
Yuto Nishida, Naoki Shikoda, Yosuke Kishinami +4
Understanding what kinds of factual knowledge large language models (LLMs) memorize is essential for evaluating their reliability and limitations. Entity-based QA is a common frame…
XQ-MEval: A Dataset with Cross-lingual Parallel Quality for Benchmarking Translation Metrics
Jingxuan Liu, Zhi Qu, Jin Tei +3
Automatic evaluation metrics are essential for building multilingual translation systems. The common practice of evaluating these systems is averaging metric scores across language…
Identifying Influential N-grams in Confidence Calibration via Regression Analysis
Shintaro Ozaki, Wataru Hashimoto, Hidetaka Kamigaito +2
While large language models (LLMs) improve performance by explicit reasoning, their responses are often overconfident, even though they include linguistic expressions demonstrating…
StructLens: A Structural Lens for Language Models via Maximum Spanning Trees
Haruki Sakajo, Frederikus Hudi, Yusuke Sakai +2
Language exhibits inherent structures, a property that explains both language acquisition and language change. Given this characteristic, we expect language models to manifest thei…
HalluCitation Matters: Revealing the Impact of Hallucinated References with 300 Hallucinated Papers in ACL Conferences
Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
Recently, we have often observed hallucinated citations or references that do not correspond to any existing work in papers under review, preprints, or published papers. Such hallu…
Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs
Soichiro Murakami, Hidetaka Kamigaito, Hiroya Takamura +1
Humor preferences vary widely across individuals and cultures, complicating the evaluation of humor using large language models (LLMs). In this study, we model heterogeneity in hum…
Routing by Analogy: kNN-Augmented Expert Assignment for Mixture-of-Experts
Boxuan Lyu, Soichiro Murakami, Hidetaka Kamigaito +1
Mixture-of-Experts (MoE) architectures scale large language models efficiently by employing a parametric ``router'' to dispatch tokens to a sparse subset of experts. Typically, thi…
Accurate and Diverse Recommendations via Propensity-Weighted Linear Autoencoders
Kazuma Onishi, Katsuhiko Hayashi, Hidetaka Kamigaito
In real-world recommender systems, user-item interactions are Missing Not At Random (MNAR), as interactions with popular items are more frequently observed than those with less pop…
Minimum Bayes Risk Decoding for Error Span Detection in Reference-Free Automatic Machine Translation Evaluation
Boxuan Lyu, Haiyue Song, Hidetaka Kamigaito +5
Error Span Detection (ESD) extends automatic machine translation (MT) evaluation by localizing translation errors and labeling their severity. Current generative ESD methods typica…
Agreement-Constrained Probabilistic Minimum Bayes Risk Decoding
Koki Natsumi, Hiroyuki Deguchi, Yusuke Sakai +2
Minimum Bayes risk (MBR) decoding generates high-quality translations by maximizing the expected utility of output candidates, but it evaluates all pairwise scores over the candida…
Toward Automatic Safe Driving Instruction: A Large-Scale Vision Language Model Approach
Haruki Sakajo, Hiroshi Takato, Hiroshi Tsutsui +3
Large-scale Vision Language Models (LVLMs) exhibit advanced capabilities in tasks that require visual information, including object detection. These capabilities have promising app…