Wei, Furu

Named entity recognition for tweets

Liu, X., Wei, F., Zhang, S., & Zhou, M. (2013) · ACM Transactions on Intelligent Systems and Technology

Topic ModelingNatural Language Processing Techniques

Cited by 61

A Length-Extrapolatable Transformer

Sun, Y., Dong, L., Patra, B., Ma, S., Huang, S., Benhaim, A., Chaudhary, V., Song, X., & Wei, F. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 59

Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation

Xia, H., Ge, T., Wang, P., Chen, S.-Q., Wei, F., & Sui, Z. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Natural Language Processing TechniquesTopic Modeling

Cited by 40

Knowledge Neurons in Pretrained Transformers

Dai, D., Dong, L., Hao, Y., Sui, Z., Chang, B., & Wei, F. (2021) · arXiv

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 39

Multiview Identifiers Enhanced Generative Retrieval

Li, Y., Yang, N., Wang, L., Wei, F., & Li, W. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Information Retrieval and Search BehaviorTopic Modeling

Cited by 37

UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation

Cheng, D., Huang, S., Bi, J., Zhan, Y., Liu, J., Wang, Y., Sun, H., Wei, F., Deng, W., & Zhang, Q. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 32

Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Dai, D., Sun, Y., Dong, L., Hao, Y., Ma, S., Sui, Z., & Wei, F. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 31

SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing

Ao, J., Wang, R., Zhou, L., Wang, C., Ren, S., Wu, Y., Liu, S., Ko, T., Li, Q., Zhang, Y., et al. (2021) · arXiv

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 30

xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token

Cheng, X., Wang, X., Zhang, X., Ge, T., Chen, S.-Q., Wei, F., Zhang, H., & Zhao, D. (2024) · Advances in Neural Information Processing Systems 37

Topic ModelingInformation Retrieval and Search Behavior

Cited by 27

Learning to Rank in Generative Retrieval

Li, Y., Yang, N., Wang, L., Wei, F., & Li, W. (2023) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 27

Learning to Rank in Generative Retrieval

Li, Y., Yang, N., Wang, L., Wei, F., & Li, W. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence

Information Retrieval and Search BehaviorTopic Modeling

Cited by 27

BitNet: Scaling 1-bit Transformers for Large Language Models

Wang, H., Ma, S., Dong, L., Huang, S., Wang, H., Ma, L., Yang, F., Wang, R., Wu, Y., & Wei, F. (2023) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 26

Differential Transformer

Ye, T., Dong, L., Xia, Y., Sun, Y., Zhu, Y., Huang, G., & Wei, F. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 23

Instruction Pre-Training: Language Models are Supervised Multitask Learners

Cheng, D., Gu, Y., Huang, S., Bi, J., Huang, M., & Wei, F. (2024) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 20

Generative Representational Instruction Tuning

Muennighoff, N., Su, H., Wang, L., Yang, N., Wei, F., Yu, T., Singh, A., & Kiela, D. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 16

Calibrating LLM-Based Evaluator

Liu, Y., Yang, T., Huang, S., Zhang, Z., Huang, H., Wei, F., Deng, W., Sun, F., & Zhang, Q. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 12

Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration

Wang, Z., Mao, S., Wu, W., Ge, T., Wei, F., & Ji, H. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 11

In-context Autoencoder for Context Compression in a Large Language Model

Ge, T., Hu, J., Wang, L., Wang, X., Chen, S.-Q., & Wei, F. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 11

Lossless Acceleration for Seq2seq Generation with Aggressive Decoding

Ge, T., Xia, H., Sun, X., Chen, S.-Q., & Wei, F. (2022) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 6

ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents

Mao, S., Cai, Y., Xia, Y., Wu, W., Wang, X., Wang, F., Ge, T., & Wei, F. (2023) · arXiv

Game Theory and ApplicationsArtificial Intelligence in Games

Cited by 3

UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation

Cheng, D., Huang, S., Bi, J., Zhan, Y., Liu, J., Wang, Y., Sun, H., Wei, F., Deng, D., & Zhang, Q. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 2

Reasoning with Exploration: An Entropy Perspective

Cheng, D., Huang, S., Zhu, X., Dai, B., Zhao, W. X., Zhang, Z., & Wei, F. (2025) · arXiv

Reinforcement Learning in RoboticsTopic Modeling

Cited by 1

Self-Boosting Large Language Models with Synthetic Preference Data

Dong, Q., Dong, L., Zhang, X., Sui, Z., & Wei, F. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1

Agensh: Scaling Organizational Intelligence to 1,024 Agents

Zhan, Z., Song, T., Dong, L., Huang, S., Lian, J., Xia, Y., & Wei, F. (2026) · arXiv

Multi-Agent Systems and NegotiationParallel Computing and Optimization Techniques

Cited by 0