2 citations · 3 across the 9 of their papers we have counts for
8 papers · 1 filter
APRiL: An Open-ended Benchmark for Chinese Social Awareness in Large Language Models
Tian Lan, Han Zhang, Shanshan Wang +3
Large language models are increasingly used in social interactions, where appropriate responses require recognizing implicit cues, inferring mental states, identifying conflicts, a…
Do Large Language Models Perform Well on Comprehending Poetic Logic in Modern Chinese Poetry?
Tian Lan, Shanshan Wang, Zehua Duo +4
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, part…
A Heuristic Perspective on Debiasing Language Models
Tian Lan, Yemin Wang, Chuancheng Shi +6
Language models (LMs) often acquire various biases during pre-training and may express them in interactions, potentially causing social harm. Existing methods often rely on counter…
Training-Inference Consistent Segmented Execution for Long-Context LLMs
Xianpeng Shang, Jiang Li, Zehua Duo +2
Transformer-based large language models face severe scalability challenges in long-context generation due to the computational and memory costs of full-context attention. Under pra…
Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language
Dianqing Lin, Tian Lan, Jiali Zhu +7
While large language models (LLMs) have achieved remarkable success in general language tasks, their performance on Chouxiang Language, a representative subcultural language in the…
Who Wrote This Line? Evaluating the Detection of LLM-Generated Classical Chinese Poetry
Jiang Li, Tian Lan, Shanshan Wang +5
The rapid development of large language models (LLMs) has extended text generation tasks into the literary domain. However, AI-generated literary creations has raised increasingly…
McBE: A Multi-task Chinese Bias Evaluation Benchmark for Large Language Models
Tian Lan, Xiangdong Su, Xu Liu +4
As large language models (LLMs) are increasingly applied to various NLP tasks, their inherent biases are gradually disclosed. Therefore, measuring biases in LLMs is crucial to miti…
TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation
Jiang Li, Xiangdong Su, Fujun Zhang +1
This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional tra…