Papers by Ukyo Honda
A Single Linear Layer Yields Task-Adapted Low-Rank Matrices (2024.lrec-main)
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| Challenge: | Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning method that updates initial weight matrix W0 with a delta matrix W . |
| Approach: | They propose a method that updates initial weight matrix W0 with a delta matrix W consisting of two low-rank matrices A and B. |
| Outcome: | The proposed method maintains a performance on par with LoRA despite the fact that the trainable parameters of CondLoRA are fewer than those of LoRA. |
Pruning Basic Elements for Better Automatic Evaluation of Summaries (N18-2)
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| Challenge: | Summarization studies work on increasing the scores that are given by automatic evaluation measures. |
| Approach: | They propose a simple but highly effective automatic evaluation measure of summarization, pruned Basic Elements. |
| Outcome: | The proposed measure outperforms ROUGE and BE in most cases and achieves highest correlation coefficient in TAC 2011 AESOP task. |
BiCSRouter: Bi-Level Cross-System Routing for Utility-Aware LLM Inference (2026.findings-acl)
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| Challenge: | Existing routing frameworks operate within a single computational paradigm . a cross-system routing framework that integrates two orthogonal regimes is proposed . |
| Approach: | They propose a cross-system routing framework that integrates two orthogonal regimes . they propose MBPP-based model that decomposes routing into intra-regime configuration selection and inter-regem system selection . |
| Outcome: | The proposed framework outperforms 15 representative baselines on MBPP and MATH benchmarks. |
CAMERA³: An Evaluation Dataset for Controllable Ad Text Generation in Japanese (2024.lrec-main)
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| Challenge: | Despite numerous efforts in ad text generation, the aspect of diversifying a text has received limited attention, particularly in non-English languages like Japanese. |
| Approach: | They present a dataset for ad text generation in Japanese using annotators to examine the capabilities of recent NLG models. |
| Outcome: | The proposed dataset includes 3,980 ad texts written by experts taking into account various aspects of ade appeals. |
On the True Distribution Approximation of Minimum Bayes-Risk Decoding (2024.naacl-short)
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| Challenge: | Minimum Bayes-risk (MBR) decoding has recently gained renewed attention in text generation. |
| Approach: | They propose to use anomaly detection to measure the degree of approximation by sampling texts from a model and selecting the text with the highest similarity to the others. |
| Outcome: | The proposed method shows that previous hypotheses about samples do not correlate well with the variation, but the results support the core assumption of MBR decoding. |
Reinforcement Learning for Edit-Based Non-Autoregressive Neural Machine Translation (2024.naacl-srw)
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| Challenge: | Non-autoregressive (NAR) language models have a performance gap due to the large decoding space and difficulty in capturing dependency between target words accurately. |
| Approach: | They propose to use reinforcement learning to enhance the performance of edit-based NAR models by using stepwise reward maximization and episodic reward maximisation. |
| Outcome: | The proposed model outperforms autoregressive models in the evaluation of an edit-based model. |
Does Self-Consistency Improve the Recall of Encyclopedic Knowledge? (2026.acl-short)
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| Challenge: | a lack of evaluation grounds for self-consistency on symbolic reasoning is unclear . however, it is unclear whether it improves performance on non-math questions involving encyclopedic knowledge. |
| Approach: | They establish a knowledge recall split for the popular MMLU benchmark by applying a data-driven heuristic from prior work. |
| Outcome: | The proposed knowledge recall split achieves an 89% accuracy on the MMLU benchmark. |
Exploring Explanations Improves the Robustness of In-Context Learning (2025.acl-long)
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| Challenge: | In-context learning (ICL) has been shown to be effective across a variety of tasks, but it has been reported to be restricted in its ability to generalize beyond the given demonstrations. |
| Approach: | They propose a framework that extends ICL by exploring explanations for all possible labels. |
| Outcome: | The proposed framework improves prediction reliability by exploring explanations for all possible labels. |
Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding (2024.findings-acl)
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| Challenge: | Existing decoding algorithms to generate diverse outputs are based on beam search or random sampling, thus their output quality is capped by these underlying decoding methods. |
| Approach: | They propose to add a diversity penalty to MBR decoding and a clustering problem to create diversity-promoting decoding algorithms by enforcing diversity objectives. |
| Outcome: | The proposed method achieves a better trade-off than the diverse beam search and sampling algorithms overall. |
Distilling Many-Shot In-Context Learning into a Cheat Sheet (2025.findings-emnlp)
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| Challenge: | Recent advances in large language models (LLMs) enable effective in-context learning with many-shot examples, but at the cost of high computational demand due to longer input tokens. |
| Approach: | proposed cheat-sheet ICL distills information from many-shot ICL into a concise textual summary . experiment shows cheat- sheet ICL achieves comparable or better performance than many- shot ICL . |
| Outcome: | Experiments on reasoning tasks show that cheat-sheet ICL achieves comparable or better performance than many-shot ICL with far fewer tokens. |
Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning (2021.eacl-main)
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| Challenge: | Unsupervised image captioning is a challenging task that requires manual annotation. |
| Approach: | They propose a simple gating mechanism that is trained to align image features with the most reliable words in pseudo-captions. |
| Outcome: | The proposed method outperforms the previous methods without complex learning objectives. |
Annotation-Efficient Language Model Alignment via Diverse and Representative Response Texts (2025.findings-emnlp)
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| Challenge: | obtaining large amount of preference annotations is difficult in many applications . obtaining a large amount is difficult, so a preference dataset needs limited annotation budget . |
| Approach: | They propose annotating preference over a subset of responses that maximizes diversity and representativeness from available responses and then annotates preference over the selected ones. |
| Outcome: | The proposed method outperforms baselines with the same annotation budget. |