1 citations · 2 across the 11 of their papers we have counts for
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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…