Papers by Ryota Miyano
Adaptive LoRA Merge with Parameter Pruning for Low-Resource Generation (2025.findings-acl)
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| Challenge: | Existing methods for adapting LLMs to low-resource tasks keep LoRA parameters frozen and the low-level problem out of their scope. |
| Approach: | They propose a LoRA merge method that updates and prunes LoRA parameters through fine-tuning with minimal target task data. |
| Outcome: | The proposed method improves performance on a low-resource language generation task and improves on previous methods. |
Self-Ensemble of N-best Generation Hypotheses by Lexically Constrained Decoding (2023.emnlp-main)
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| Challenge: | Existing studies have improved generation quality by explicitly reranking N-best candidates. |
| Approach: | They propose a method that ensembles N-best hypotheses to improve natural language generation by combining high-quality fragments of N- best hypothese . they use tokens that should or should not be present in the final output as lexical constraints to improve quality of generation. |
| Outcome: | Empirical results show that the proposed method outperforms strong N-best reranking methods on paraphrase generation, summarisation, and constrained text generation. |