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cs.CL2026

Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models

Haoran Wang, Xiongxiao Xu, Philip S. Yu +1

Large language models (LLMs) and large vision-language models (LVLMs) have demonstrated impressive generative capabilities, yet ensuring their outputs align with user intent is sti…

cs.CL2025

MathAgent: Leveraging a Mixture-of-Math-Agent Framework for Real-World Multimodal Mathematical Error Detection

Yibo Yan, Shen Wang, Jiahao Huo +3

Mathematical error detection in educational settings presents a significant challenge for Multimodal Large Language Models (MLLMs), requiring a sophisticated understanding of both…

cs.CL20252 cited

A Survey on Post-training of Large Language Models

Guiyao Tie, Zeli Zhao, Dingjie Song +23

The emergence of Large Language Models (LLMs) has fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational system…

cs.CL2025

Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?

Leyi Pan, Aiwei Liu, Shiyu Huang +5

The radioactive nature of Large Language Model (LLM) watermarking enables the detection of watermarks inherited by student models when trained on the outputs of watermarked teacher…

cs.CL2025

Position: LLMs Can be Good Tutors in English Education

Jingheng Ye, Shen Wang, Deqing Zou +8

While recent efforts have begun integrating large language models (LLMs) into English education, they often rely on traditional approaches to learning tasks without fully embracing…

cs.CL2025

Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning

Yibo Yan, Shen Wang, Jiahao Huo +7

Scientific reasoning, the process through which humans apply logic, evidence, and critical thinking to explore and interpret scientific phenomena, is essential in advancing knowled…