Author

Jiang Li

Inner Mongolia University

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Preprint Aug 2026

Do Large Language Models Perform Well on Comprehending Poetic Logic in Modern Chinese Poetry?

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

#natural language process... Preprint Oct 2026

Acmite: Mitigating Gender Bias in LLMs through Concept-Guided Mutual Information

Large language models (LLMs) can reproduce social stereotypes from their training data, motivating extensive research on model debiasing. However, existing methods often rely on explicit biased examples or predefined group-term substitutions, making them sensitive to wording and less effective at capturing stereotype c...

Tian Lan, Xiao-Qing Cheng, Han Zhang et al. · 0 citations

#machine learning Preprint Sep 2026

Distance-KV: Exploiting Relative Distance for Efficient Long-Context Inference

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

Preprint Aug 2026

A Heuristic Perspective on Debiasing Language Models

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