Papers by Sho Hoshino
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. |
Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity (2024.lrec-main)
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| Challenge: | Using labeled NLI datasets for learning sentence embeddings leads to improved performance for natural language understanding tasks. |
| Approach: | They compare two data augmentation techniques for learning better sentence embeddings . they use a cross-lingual transfer technique that exploits English resources as training data to yield non-English sentence embeds as zero-shot inference . |
| Outcome: | The proposed techniques yield better performance on Japanese and Korean sentences. |
Aspect-based Analysis of Advertising Appeals for Search Engine Advertising (2022.naacl-industry)
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| Challenge: | ad creators must consider various aspects of advertising appeals such as price, product features, and quality in their ac work. |
| Approach: | They propose to use a dataset of ad texts to explore the effective aspects of advertising appeals (A3) for different industries to assist a search engine ap creators. |
| Outcome: | The proposed model can detect aspects of ad texts and help them estimate their performance. |
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. |