1 citations · 2 across the 15 of their papers we have counts for
4 papers · 1 filter
ReFF: Reinforcing Format Faithfulness in Language Models across Varied Tasks
Jiashu Yao, Heyan Huang, Zeming Liu +4
Following formatting instructions to generate well-structured content is a fundamental yet often unmet capability for large language models (LLMs). To study this capability, which…
Optimizing Chain-of-Thought Reasoning: Tackling Arranging Bottleneck via Plan Augmentation
Yuli Qiu, Jiashu Yao, Heyan Huang +1
Multi-step reasoning ability of large language models is crucial in tasks such as math and tool utilization. Current researches predominantly focus on enhancing model performance i…
FAME: Towards Factual Multi-Task Model Editing
Li Zeng, Yingyu Shan, Zeming Liu +2
Large language models (LLMs) embed extensive knowledge and utilize it to perform exceptionally well across various tasks. Nevertheless, outdated knowledge or factual errors within…
Deterministic Reversible Data Augmentation for Neural Machine Translation
Jiashu Yao, Heyan Huang, Zeming Liu +1
Data augmentation is an effective way to diversify corpora in machine translation, but previous methods may introduce semantic inconsistency between original and augmented data bec…