7 papers
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…
Breaking Dual Bottlenecks: Evolving Unified Multimodal Models into Self-Adaptive Interleaved Visual Reasoners
Qingyang Liu, Bingjie Gao, Canmiao Fu +9
Recent unified models integrate multimodal understanding and generation within a single framework. However, an "understanding-generation gap" persists, where models can capture use…
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…