3 citations · 4 across the 4 of their papers we have counts for
4 papers
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…
Paraphrase-Aligned Machine Translation
Ke-Ching Chang, Chung-Chi Chen, An-Zi Yen
Large Language Models (LLMs) have demonstrated significant capabilities in machine translation. However, their translation quality is sometimes questioned, as the generated outputs…
NYK-MS: A Well-annotated Multi-modal Metaphor and Sarcasm Understanding Benchmark on Cartoon-Caption Dataset
Ke Chang, Hao Li, Junzhao Zhang +1
Metaphor and sarcasm are common figurative expressions in people's communication, especially on the Internet or the memes popular among teenagers. We create a new benchmark named N…
Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment
Ming Zhang, Ke Chang, Yunfang Wu
Multi-modal semantic understanding requires integrating information from different modalities to extract users' real intention behind words. Most previous work applies a dual-encod…