1 citations · 2 across the 11 of their papers we have counts for
12 papers
FlexComp: One Model for Every Ratio in Context Compression
Kaiyan Zhao, Zhongtao Miao, Akiko Aizawa +1
Soft context compression condenses a context into a few memory tokens that a frozen LLM consumes in place of the raw text, but existing compressors fix the compression ratio at tra…
CNeo-Bench: Diagnosing Large Language Models on Chinese Neologisms
Kaiyan Zhao, Zhongtao Miao, Zheyong Xie +2
Chinese neologisms exploit diverse and unique linguistic mechanisms, such as phonetic substitution (e.g., 886 for ``bye-bye'') and visual character decomposition that are rare in o…
RegMix-D: Dynamic Data Mixing via Proxy Training Trajectories
Kaiyan Zhao, Zhongtao Miao, Akiko Aizawa +1
Data mixture selection is critical for Large Language Model pretraining. Existing methods such as RegMix select a single static mixture by fitting a regression model on small-scale…
GRC: Unifying Reasoning-Driven Generation, Retrieval and Compression
Zhongtao Miao, Qiyu Wu, Yoshimasa Tsuruoka
Text embedding and generative tasks are usually trained separately based on large language models (LLMs) nowadays. This causes a large amount of training cost and deployment effort…
NeoAMT: Neologism-Aware Agentic Machine Translation with Reinforcement Learning
Zhongtao Miao, Kaiyan Zhao, Masaaki Nagata +1
Neologism-aware machine translation aims to translate source sentences containing neologisms into target languages. This field remains underexplored compared with general machine t…
Improving Multimodal Contrastive Learning of Sentence Embeddings with Object-Phrase Alignment
Kaiyan Zhao, Zhongtao Miao, Yoshimasa Tsuruoka
Multimodal sentence embedding models typically leverage image-caption pairs in addition to textual data during training. However, such pairs often contain noise, including redundan…