Yang, Yujiu

Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Liang, T., He, Z., Jiao, W., Wang, X., Wang, Y., Wang, R., Yang, Y., Shi, S., & Tu, Z. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 1,062

CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

Gou, Z., Shao, Z., Gong, Y., Shen, Y., Yang, Y., Duan, N., & Chen, W. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 561

TediGAN: Text-Guided Diverse Face Image Generation and Manipulation

Xia, W., Yang, Y., Xue, J.-H., & Wu, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Generative Adversarial Networks and Image SynthesisDigital Media Forensic Detection

Cited by 376

Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Liang, T., He, Z., Jiao, W., Wang, X., Wang, Y., Wang, R., Yang, Y., Shi, S., & Tu, Z. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 320

EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers

Guo, Q., Wang, R., Guo, J., Li, B., Song, K., Tan, X., Liu, G., Bian, J., & Yang, Y. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 50

ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving

Gou, Z., Shao, Z., Gong, Y., Shen, Y., Yang, Y., Huang, M., Duan, N., & Chen, W. (2023) · arXiv

Topic ModelingMathematics, Computing, and Information Processing

Cited by 24

CriticBench: Benchmarking LLMs for Critique-Correct Reasoning

Lin, Z., Gou, Z., Liang, T., Luo, R., Liu, H., & Yang, Y. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 16

SciAgent: Tool-augmented Language Models for Scientific Reasoning

Ma, Y., Gou, Z., Hao, J., Xu, R., Wang, S., Pan, L., Yang, Y., Cao, Y., & Sun, A. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingMultimodal Machine Learning Applications

Cited by 14

Not All Tokens Are What You Need for Pretraining

Lin, Z., Gou, Z., Gong, Y., Liu, X., Shen, Y., Xu, R., Lin, C., Yang, Y., Jiao, J., Duan, N., et al. (2024) · Advances in Neural Information Processing Systems 37

Topic ModelingNatural Language Processing Techniques

Cited by 9

ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation

Yang, C., Shi, C., Liu, Y., Shui, B., Wang, J., Jing, M., Xu, L., Zhu, X., Li, S., Zhang, Y., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 4

TaleCrafter: Interactive Story Visualization with Multiple Characters

Gong, Y., Pang, Y., Cun, X., Xia, M., He, Y., Chen, H., Wang, L., Zhang, Y., Wang, X., Shan, Y., et al. (2023) · arXiv

Generative Adversarial Networks and Image SynthesisDigital Media and Visual Art

Cited by 3

SciAgent: Tool-augmented Language Models for Scientific Reasoning

Ma, Y., Gou, Z., Hao, J., Xu, R., Wang, S., Pan, L., Yang, Y., Cao, Y., Sun, A., Awadalla, H., et al. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 2