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20242026
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cs.CL2026

Reinforced Curriculum Pre-Alignment for Domain-Adaptive VLMs

Yuming Yan, Shuo Yang, Kai Tang +7

Vision-Language Models (VLMs) demonstrate remarkable general-purpose capabilities but often fall short in specialized domains such as medical imaging or geometric problem-solving.…

cs.CL2026

Gender and Race Bias in Consumer Product Recommendations by Large Language Models

Ke Xu, Shera Potka, Alex Thomo

Large Language Models are increasingly employed in generating consumer product recommendations, yet their potential for embedding and amplifying gender and race biases remains unde…

cs.CL2025

MIO: A Foundation Model on Multimodal Tokens

Zekun Wang, King Zhu, Chunpu Xu +14

In this paper, we introduce MIO, a novel foundation model built on multimodal tokens, capable of understanding and generating speech, text, images, and videos in an end-to-end, aut…

cs.CL2024

PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment

Zekun Moore Wang, Shawn Wang, Kang Zhu +5

Alignment of large language models (LLMs) involves training models on preference-contrastive output pairs to adjust their responses according to human preferences. To obtain such c…

cs.CL2024

PositionID: LLMs can Control Lengths, Copy and Paste with Explicit Positional Awareness

Zekun Wang, Feiyu Duan, Yibo Zhang +4

Large Language Models (LLMs) demonstrate impressive capabilities across various domains, including role-playing, creative writing, mathematical reasoning, and coding. Despite these…

cs.CL2024

RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models

Zekun Moore Wang, Zhongyuan Peng, Haoran Que +14

The advent of Large Language Models (LLMs) has paved the way for complex tasks such as role-playing, which enhances user interactions by enabling models to imitate various characte…