Papers by Katsuhiko Hayashi
Diversity Explains Inference Scaling Laws: Through a Case Study of Minimum Bayes Risk Decoding (2025.acl-long)
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| Challenge: | Existing studies show that MBR decoding improves model generation performance . however, the theoretical underpinnings of these results remain uncertain . |
| Approach: | They propose a theoretical interpretation of MBR decoding from the perspective of bias–diversity decomposition. |
| Outcome: | The proposed method improves the quality estimation of hypotheses by decomposing bias and diversity into two main factors. |
Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding (2021.acl-long)
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| Challenge: | Existing knowledge graph embedding methods do not provide a fairly accurate comparison of the two loss functions. |
| Approach: | They propose to use the Bregman divergence to provide a unified interpretation of the softmax cross-entropy and negative sampling loss functions. |
| Outcome: | The proposed model can be used to predict missing relational links between entities using a scoring method. |
Analyzing Word Embedding Through Structural Equation Modeling (2020.lrec-1)
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| Challenge: | Existing studies have shown that word embedding improves accuracy on NLP tasks. |
| Approach: | They propose a causal diagram based on the evaluation results of word embeddings using partial least squares path modeling. |
| Outcome: | The proposed model proves that word embedding contributes to solving downstream tasks. |
Neural Tensor Networks with Diagonal Slice Matrices (N18-1)
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| Challenge: | A large number of parameters can cause overfitting and a long training time for neural tensor networks (NTNs). |
| Approach: | They propose two new parameter reduction techniques to reduce the number of parameters in an NTN without diminishing its expressiveness. |
| Outcome: | The proposed models learn better and faster than the original (R)NTNs. |
Towards Cross-Lingual Explanation of Artwork in Large-scale Vision Language Models (2025.findings-naacl)
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| Challenge: | LVLMs are increasingly capable of responding in multiple languages . however, there is a lack of evaluation tools for LVLs that handle multiple languages. |
| Approach: | They used an extended dataset in multiple languages to evaluate LVLMs' ability to generate explanations in multiple language combinations. |
| Outcome: | The proposed dataset in multiple languages evaluates LVLMs' ability to generate explanations in other languages. |
IRR: Image Review Ranking Framework for Evaluating Vision-Language Models (2025.coling-main)
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Kazuki Hayashi, Kazuma Onishi, Toma Suzuki, Yusuke Ide, Seiji Gobara, Shigeki Saito, Yusuke Sakai, Hidetaka Kamigaito, Katsuhiko Hayashi, Taro Watanabe
| Challenge: | Large-scale vision language models excel at generating factual content, but their ability to rank images from multiple perspectives has not been explored. |
| Approach: | They propose a framework to evaluate large-scale vision-language models by measuring their ability to rank image texts from multiple perspectives. |
| Outcome: | The proposed evaluation framework measures how closely LVLMs' judgments align with human interpretations. |
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion? (2024.naacl-long)
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| Challenge: | Knowledge Graph Completion (KGC) is a task that infers unseen relationships between entities . traditional embedding-based methods infer missing links using only training data . a pre-trained language model (PLM)-based KGC may be ineffective in practical applications . |
| Approach: | They propose to use knowledge Graph Completion (KGC) to infer unseen relationships . traditional embedding-based KGC methods infer missing links only from training data . they argue that pre-trained language models acquire inference abilities through pre-training . |
| Outcome: | The proposed method improves performance even though it does not use memorized knowledge. |
Towards Artwork Explanation in Large-scale Vision Language Models (2024.acl-short)
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| Challenge: | Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating advanced capabilities in text generation and comprehension. |
| Approach: | They propose to use artwork explanation generation task to quantitatively assess the understanding and utilization of artworks knowledge. |
| Outcome: | The proposed task evaluates the understanding and utilization of knowledge about artworks from images and titles and generates explanations using only images. |
Beyond Sampling: Self-Sorting for Long-Context Ranking (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) remain unstable on long-context ranking. |
| Approach: | They propose a method that fuses explicit within-list positions with implicit cross-list preferences to score entities and return a top-k set. |
| Outcome: | Experimental results show that large language models remain unstable on long-context ranking . |
Table and Image Generation for Investigating Knowledge of Entities in Pre-trained Vision and Language Models (2023.acl-short)
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| Challenge: | Existing knowledge about entities acquired from natural language models is not retained in pre-trained vision & language models. |
| Approach: | They propose a task to verify how knowledge about entities acquired from natural language is retained in Vision & Language (V&L) models. |
| Outcome: | The proposed model forgets part of its entity knowledge by pre-training to improve image related tasks. |
Higher-Order Syntactic Attention Network for Longer Sentence Compression (N18-1)
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| Challenge: | Existing sentence compression methods do not handle syntactic features, causing performance degradation . et al. (2015) reported that the longer the input sentences are, the worse the performance becomes. |
| Approach: | They propose a higher-order syntactic attention network that handles higher-level dependency features as an attention distribution on LSTM hidden states. |
| Outcome: | The proposed method outperforms baseline methods on a Google sentence compression dataset. |
A Non-commutative Bilinear Model for Answering Path Queries in Knowledge Graphs (D19-1)
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| Challenge: | Knowledge graph embedding (KGE) is a promising approach to knowledge graph completion. |
| Approach: | They propose a bilinear KGE model based on block circulant matrices that is non-commutative and can be modeled by matrix product. |
| Outcome: | The proposed model can be used to model composite relations on a spectrum from diagonal to full relation matrices. |
A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings (2020.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding models represent entities and relations as real or complexvalued vectors thus consuming a large amount of memory. |
| Approach: | They propose a discrete optimization method for training binarized knowledge graph embedding model B-CP. |
| Outcome: | The proposed method performs comparable to existing models on benchmark tasks. |