Large Language Models (LLMs) have achieved significant progress across a wide range of natural language processing (NLP) tasks, yet their ability to understand literary texts, particularly modern Chinese poetry, remains largely unexplored. The unique literary characteristics of modern Chinese poetry necessitate a disti...
Tian Lan, Shanshan Wang, Zehua Duo et al.· 0 citations
The memory usage and decoding latency of LLM inference grow rapidly with context length. To reduce these costs, key-value (KV) cache compression methods selectively retain cached states based on token importance or differences in attention patterns across heads. However, we discover that retrieval capability varies sub...
Xian-Peng Shang, Can-Bin Huang, Jiang Li et al.· 0 citations
HEIMAT, a HEurIstic-style autoMATic debiasing framework for LMs, effectively mitigates bias in different cultures while maintaining the model's natural language understanding (NLU) performance.
Tian Lan, Yemin Wang, Chuan-Cheng Shi et al.· 0 citations
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