Self-Instruct: Aligning Language Models with Self-Generated Instructions

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works

Distilling the Knowledge in a Neural Network

Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works

Training language models to follow instructions with human feedback

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works

Training Language Models to Follow Instructions with Human Feedback

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2011 · Cited by 3,117 · see citing works

The Power of Scale for Parameter-Efficient Prompt Tuning

Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096 · see citing works

The Power of Scale for Parameter-Efficient Prompt Tuning

Lester, B., Al-Rfou, R., & Constant, N. (2021) · arXiv · doi:10.48550/arxiv.2104.08691 · Cited by 3,092

Self-Training With Noisy Student Improves ImageNet Classification

Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01070 · Cited by 2,393 · see citing works

Scaling Instruction-Finetuned Language Models

Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.11416 · Cited by 1,441 · see citing works

Finetuned Language Models Are Zero-Shot Learners

Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2021) · arXiv · doi:10.48550/arxiv.2109.01652 · Cited by 1,101 · see citing works

A Survey of Data Augmentation Approaches for NLP

Feng, S. Y., Gangal, V., Wei, J., Chandar, S., Vosoughi, S., Mitamura, T., & Hovy, E. H. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.84 · Cited by 802 · see citing works

ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

Shridhar, M., Thomason, J., Gordon, D., Bisk, Y., Han, W., Mottaghi, R., Zettlemoyer, L., & Fox, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01075 · Cited by 540 · see citing works

Unnatural Instructions: Tuning Language Models with (Almost) No Human LaborPreprint

Honovich, O., Scialom, T., Levy, O., & Schick, T. (2022) · Annual Meeting of the Association for Computational Linguistics · doi:10.48550/arxiv.2212.09689 · Cited by 516 · see citing works

Constituency Parsing with a Self-Attentive Encoder

Kitaev, N., & Klein, D. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1249 · Cited by 483 · see citing works

Large Language Models Are Human-Level Prompt Engineers

Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. (2022) · arXiv · doi:10.48550/arxiv.2211.01910 · Cited by 330 · see citing works

Speaker-Follower Models for Vision-and-Language Navigation

Fried, D., Hu, R., Cirik, V., Rohrbach, A., Andreas, J., Morency, L.-P., Berg-Kirkpatrick, T., Saenko, K., Klein, D., & Darrell, T. (2018) · arXiv · doi:10.48550/arxiv.1806.02724 · Cited by 291 · see citing works

Cross-Task Generalization via Natural Language Crowdsourcing Instructions

Mishra, S., Khashabi, D., Baral, C., & Hajishirzi, H. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.244 · Cited by 269 · see citing works

Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

Wang, Y., Mishra, S., Alipoormolabashi, P., Kordi, Y., Mirzaei, A., Naik, A., Ashok, A., Dhanasekaran, A. S., Arunkumar, A., Stap, D., et al. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.340 · Cited by 247 · see citing works

Large Language Models Can Self-Improve

Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., & Han, J. (2022) · arXiv · doi:10.48550/arxiv.2210.11610 · Cited by 230 · see citing works

Large Language Models Can Self-Improve

Huang, J., Gu, S., Hou, L., Wu, Y., Wang, X., Yu, H., & Han, J. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.67 · Cited by 230 · see citing works

Multilingual Constituency Parsing with Self-Attention and Pre-Training

Kitaev, N., Cao, S., & Klein, D. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1340 · Cited by 205 · see citing works

Self-training: A survey

Amini, M.-R., Feofanov, V., Pauletto, L., Hadjadj, L., Devijver, É., & Maximov, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128904 · Cited by 192 · see citing works

Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data

Xu, C., Guo, D., Duan, N., & McAuley, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.385 · Cited by 191 · see citing works

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

Bach, S., Sanh, V., Yong, Z. X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., et al. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2022.acl-demo.9 · Cited by 178 · see citing works

Symbolic Knowledge Distillation: from General Language Models to Commonsense Models

West, P., Bhagavatula, C., Hessel, J., Hwang, J. D., Jiang, L., Bras, R. J. L., Lu, X., Welleck, S., & Choi, Y. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.341 · Cited by 165 · see citing works

Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor

Honovich, O., Scialom, T., Levy, O., & Schick, T. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.806 · Cited by 160 · see citing works

Revisiting Self-Training for Neural Sequence GenerationPreprint

He, J., Gu, J., Shen, J., & Ranzato, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.13788 · Cited by 150 · see citing works

Large Language Models Struggle to Learn Long-Tail Knowledge

Kandpal, N., Deng, H., Roberts, A., Wallace, E., & Raffel, C. (2022) · arXiv · doi:10.48550/arxiv.2211.08411 · Cited by 143 · see citing works

Self-training Improves Pre-training for Natural Language Understanding

Du, J., Grave, É., Gunel, B., Chaudhary, V., Çelebi, O., Auli, M., Stoyanov, V., & Conneau, A. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.426 · Cited by 141 · see citing works

WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation

Liu, A., Swayamdipta, S., Smith, N. A., & Choi, Y. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.508 · Cited by 131 · see citing works

Teaching Small Language Models to Reason

Magister, L. C., Mallinson, J., Adamek, J., Malmi, E., & Severyn, A. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/2023.acl-short.151 · Cited by 127 · see citing works

Generating Datasets with Pretrained Language Models

Schick, T., & Schütze, H. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.555 · Cited by 122 · see citing works

Generative Data Augmentation for Commonsense Reasoning

Yang, Y., Malaviya, C., Fernandez, J., Swayamdipta, S., Bras, R. J. L., Wang, J., Bhagavatula, C., Choi, Y., & Downey, D. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.90 · Cited by 117 · see citing works

Instruction Induction: From Few Examples to Natural Language Task Descriptions

Honovich, O., Shaham, U., Bowman, S. R., & Levy, O. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.108 · Cited by 90 · see citing works

FILM: Following Instructions in Language with Modular Methods

Min, S. Y., Chaplot, D. S., Ravikumar, P., Bisk, Y., & Salakhutdinov, R. (2021) · arXiv · doi:10.48550/arxiv.2110.07342 · Cited by 81 · see citing works

Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision

Sun, Z., Shen, Y., Zhou, Q., Zhang, H., Chen, Z., Cox, D., Yang, Y., & Gan, C. (2023) · arXiv · doi:10.48550/arxiv.2305.03047 · Cited by 81 · see citing works

Towards Zero-Label Language LearningPreprint

Wang, Z.-R., Yu, A. W., Fırat, O., & Cao, Y. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.09193 · Cited by 66 · see citing works

Learning from Task Descriptions

Weller, O., Lourie, N., Gardner, M., & Peters, M. E. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.105 · Cited by 61 · see citing works

Explaining Patterns in Data with Language Models via Interpretable AutopromptingPreprint

Singh, C., Morris, J. X., Aneja, J., Rush, A. M., & Gao, J. (2022) · arXiv.org · doi:10.48550/arxiv.2210.01848 · Cited by 58 · see citing works

Impact of Pretraining Term Frequencies on Few-Shot Reasoning

Razeghi, Y., Logan, R. L., Gardner, M., & Singh, S. (2022) · arXiv · doi:10.48550/arxiv.2202.07206 · Cited by 53 · see citing works

Prompt Consistency for Zero-Shot Task Generalization

Zhou, C., He, J., Ma, X., Berg-Kirkpatrick, T., & Neubig, G. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.192 · Cited by 45 · see citing works

Pre-trained Language Models Can be Fully Zero-Shot Learners

Zhao, X., Ouyang, S., Yu, Z.-G., Wu, M.-L., & Li, L. (2022) · Annual Meeting of the Association for Computational Linguistics · doi:10.48550/arxiv.2212.06950 · Cited by 45

Generating Sequences by Learning to Self-Correct

Welleck, S., Lu, X., West, P., Brahman, F., Shen, T., Khashabi, D., & Choi, Y. (2022) · arXiv · doi:10.48550/arxiv.2211.00053 · Cited by 41 · see citing works

Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot LearningPreprint

Yu, M., Michalski, M., Huang, J., Zhang, Y., Abdelzaher, T., & Han, J. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.03044 · Cited by 32 · see citing works

Pre-trained Language Models Can be Fully Zero-Shot Learners

Zhao, X., Ouyang, S., Yu, Z., Wu, M., & Li, L. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.869 · Cited by 29 · see citing works

Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot LearnersPreprint

Ye, S., Kim, D., Jang, J., Shin, J., & Seo, M. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.02969 · Cited by 13 · see citing works

Leveraging QA Datasets to Improve Generative Data Augmentation

Mekala, D., Vu, T., Schick, T., & Shang, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.660 · Cited by 8 · see citing works

Teaching Small Language Models to Reason

Magister, L. C., Mallinson, J., Adamek, J., Malmi, E., & Severyn, A. (2022) · arXiv · doi:10.48550/arxiv.2212.08410 · Cited by 7 · see citing works

Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat DataPreprint

Xu, C., Guo, D., Duan, N., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.01196 · Cited by 7 · see citing works

Instruction Induction: From Few Examples to Natural Language Task DescriptionsPreprint

Honovich, O., Shaham, U., Bowman, S. R., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.10782 · Cited by 6 · see citing works

One-Shot Learning from a Demonstration with Hierarchical Latent Language

Weir, N., Yuan, X., Côté, M.-A., Hausknecht, M., Laroche, R., Momennejad, I., van Seijen, H., & Durme, B. V. (2023) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/kagr5160 · Cited by 4 · see citing works

One-Shot Learning from a Demonstration with Hierarchical Latent LanguagePreprint

Weir, N., Yuan, X., Côté, M.-A., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.04806 · Cited by 2 · see citing works

Self-Training: A Survey

Amini, M.-R., Feofanov, V., Pauletto, L., Hadjadj, L., Devijver, E., & Maximov, Y. (2022) · arXiv · doi:10.48550/arxiv.2202.12040

Leveraging QA Datasets to Improve Generative Data Augmentation

Mekala, D., Vu, T., Schick, T., & Shang, J. (2022) · arXiv · doi:10.48550/arxiv.2205.12604

Efficient Memory Management for Large Language Model Serving with PagedAttention

Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J., Zhang, H., & Stoica, I. (2023) · Proceedings of the 29th Symposium on Operating Systems Principles · doi:10.1145/3600006.3613165 · Cited by 1,817 · see citing works

A Survey of Large Language Models

Zhao, W. X., Zhou, K., Li, J., Tang, T., Dong, Z., Hou, Y., Zhang, B., Min, Y., Zhang, J., Liu, P., et al. (2026) · Frontiers of Computer Science · doi:10.1007/s11704-026-60308-3 · Cited by 2,093 · see citing works

A Comprehensive Overview of Large Language Models

Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3744746 · Cited by 1,257 · see citing works

A Survey of AI-Generated Content (AIGC)

Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2024) · ACM Computing Surveys · doi:10.1145/3704262 · Cited by 1,400 · see citing works

The rise and potential of large language model based agents: a survey

Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4222-0 · Cited by 970 · see citing works

A survey on multimodal large language models

Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., & Chen, E. (2024) · National Science Review · doi:10.1093/nsr/nwae403 · Cited by 893 · see citing works

Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.05685 · Cited by 1,888 · see citing works

A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT

Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., Zhang, K., Ji, C., Yan, Q., He, L., et al. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02443-6 · Cited by 704 · see citing works

A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023) · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.ijcnlp-main.45 · Cited by 682 · see citing works

A Survey on In-context Learning

Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Chang, B., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.64 · Cited by 649 · see citing works

Instruction Tuning for Large Language Models: A Survey

Zhang, S., Dong, L., Li, X., Zhang, S., Sun, X., Wang, S., Li, J., Hu, R., Zhang, T., Wang, G., et al. (2025) · ACM Computing Surveys · doi:10.1145/3777411 · Cited by 640 · see citing works

ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge

Li, Y., Li, Z., Zhang, K., Dan, R., Jiang, S. B., & Zhang, Y. (2023) · Cureus · doi:10.7759/cureus.40895 · Cited by 616 · see citing works

Jailbreaking Black Box Large Language Models in Twenty Queries

Chao, P., Robey, A., Dobriban, E., Hassani, H., Pappas, G. J., & Wong, E. (2025) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml64287.2025.00010 · Cited by 523 · see citing works

Qwen Technical Report

Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2309.16609 · Cited by 332 · see citing works

A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)

Patil, R., & Gudivada, V. N. (2024) · Applied Sciences · doi:10.3390/app14052074 · Cited by 315 · see citing works

A Survey on Model Compression for Large Language Models

Zhu, X., Li, J., Liu, Y., Ma, C., & Wang, W. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00704 · Cited by 273 · see citing works

Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey

Ling, C., Zhao, X., Lu, J., Deng, C., Zheng, C., Wang, J., Chowdhury, T., Li, Y., Cui, H., Zhang, X., et al. (2025) · ACM Computing Surveys · doi:10.1145/3764579 · Cited by 263 · see citing works

Vision-language models for medical report generation and visual question answering: a review

Hartsock, I., & Rasool, G. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1430984 · Cited by 257 · see citing works

Invited Paper: VerilogEval: Evaluating Large Language Models for Verilog Code Generation

Liu, M., Pinckney, N., Khailany, B., & Ren, H. M. (2023) · doi:10.1109/iccad57390.2023.10323812 · Cited by 253 · see citing works

Otter: A Multi-Modal Model With In-Context Instruction Tuning

Li, B., Zhang, Y., Chen, L., Wang, J., Pu, F., Cahyono, J. A., Yang, J., Li, C., & Liu, Z. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3571946 · Cited by 253 · see citing works

A Survey on Large Language Models for Code Generation

Jiang, J., Wang, F., Shen, J., Kim, S., & Kim, S. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3747588 · Cited by 251 · see citing works

GraphGPT: Graph Instruction Tuning for Large Language Models

Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., & Huang, C. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657775 · Cited by 249 · see citing works

GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis

Sun, Y., Wu, D., Xue, Y., Liu, H., Wang, H., Xu, Z., Xie, X., & Liu, Y. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3597503.3639117 · Cited by 244 · see citing works

Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach

Zhang, J., Xie, R., Hou, Y., Zhao, W. X., Lin, L., & Wen, J.-R. (2024) · ACM Transactions on Information Systems · doi:10.1145/3708882 · Cited by 241 · see citing works

HuatuoGPT, Towards Taming Language Model to Be a Doctor

Zhang, H., Chen, J., Jiang, F., Yu, F., Chen, Z., Chen, G., Li, J., Wu, X., Zhang, Z., Xiao, Q., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.725 · Cited by 223 · see citing works

MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models

Yang, K., Zhang, T., Kuang, Z., Xie, Q., Huang, J., & Ananiadou, S. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3648137 · Cited by 214 · see citing works

LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Jiang, D., Ren, X., & Lin, B. Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.792 · Cited by 207 · see citing works

Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving

Chen, L., Sinavski, O., Hünermann, J., Karnsund, A., Willmott, A. J., Birch, D., Maund, D., & Shotton, J. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611018 · Cited by 203 · see citing works

Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models

Zhang, B., Yang, H., Zhou, T., Babar, M. A., & Liu, X.-Y. (2023) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3604237.3626866 · Cited by 199 · see citing works

Structured Chain-of-Thought Prompting for Code Generation

Li, J., Li, G., Li, Y., & Jin, Z. G. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3690635 · Cited by 200 · see citing works

Automatic Prompt Optimization with “Gradient Descent” and Beam Search

Pryzant, R., Iter, D., Li, J., Lee, Y., Zhu, C., & Zeng, M. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.494 · Cited by 191 · see citing works

Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data

Xu, C., Guo, D., Duan, N., & McAuley, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.385 · Cited by 191 · see citing works

Parameter-efficient fine-tuning in large language models: a survey of methodologies

Wang, L., Chen, S., Jiang, L., Pan, S., Cai, R., Yang, S., & Yang, F. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11236-4 · Cited by 186 · see citing works

Toward large reasoning models: A survey of reinforced reasoning with large language models

Xu, F., Hao, Q., Shao, C., Zong, Z., Li, Y., Wang, J., Zhang, Y., Wang, J., Lan, X., Gong, J., et al. (2025) · Patterns · doi:10.1016/j.patter.2025.101370 · Cited by 186 · see citing works

Diffusion Model-Based Image Editing: A Survey

Huang, Y., Huang, J., Liu, Y., Yan, M., Lv, J., Liu, J., Xiong, W., Zhang, H., Cao, L., & Chen, S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3541625 · Cited by 168 · see citing works

Understanding Large-Language Model (LLM)-powered Human-Robot Interaction

Kim, C. Y., Lee, C. P., & Mutlu, B. (2024) · Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3610977.3634966 · Cited by 162 · see citing works

CodeS: Towards Building Open-source Language Models for Text-to-SQL

Li, H., Zhang, J., Liu, H., Fan, J., Zhang, X., Zhu, J., Wei, R., Pan, H., Li, C., & Chen, H. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3654930 · Cited by 160 · see citing works

Datasets for large language models: a comprehensive survey

Liu, Y., Cao, J., Liu, C., Ding, K., & Jin, L. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11403-7 · Cited by 159 · see citing works

Self-Planning Code Generation with Large Language Models

Jiang, X., Dong, Y., Wang, L., Fang, Z., Shang, Q., Li, G., Jin, Z., & Jiao, W. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3672456 · Cited by 158 · see citing works

ChatGPT: potential, prospects, and limitations

Zhou, J., Ke, P., Qiu, X., Huang, M., & Zhang, J. (2024) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2300089 · Cited by 153 · see citing works

A large-scale audit of dataset licensing and attribution in AI

Longpre, S., Mahari, R., Chen, A., Obeng-Marnu, N., Sileo, D., Brannon, W., Muennighoff, N., Khazam, N., Kabbara, J., Perisetla, K., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00878-8 · Cited by 150 · see citing works

Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-World Multi-Turn Dialogue

Yang, S., Zhao, H., Zhu, S., Zhou, G., Hongfei, X., Jia, Y., & Zan, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29907 · Cited by 145 · see citing works

OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning

Ye, R., Wang, W., Chai, J., Li, D., Li, Z., Xu, Y., Du, Y., Wang, Y., & Chen, S. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671582 · Cited by 143 · see citing works

Character-LLM: A Trainable Agent for Role-Playing

Shao, Y., Li, L., Dai, J., & Qiu, X. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.814 · Cited by 140 · see citing works

Towards Building The Federatedgpt: Federated Instruction Tuning

Zhang, J., Vahidian, S., Kuo, M., Li, C., Zhang, R., Yu, T., Wang, G., & Chen, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447454 · Cited by 140 · see citing works

An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-Tuning

Luo, Y., Yang, Z., Meng, F., Li, Y., Zhou, J., & Zhang, Y. (2025) · IEEE Transactions on Audio, Speech and Language Processing · doi:10.1109/taslpro.2025.3606231 · Cited by 136 · see citing works

An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

Luo, Y., Yang, Z., Meng, F., Li, Y., Zhou, J., & Zhang, Y. (2023) · arXiv · doi:10.48550/arxiv.2308.08747 · Cited by 135 · see citing works

NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark

Sainz, O., Campos, J., García-Ferrero, I., Etxaniz, J., de Lacalle, O. L., & Agirre, E. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.722 · Cited by 129 · see citing works

Empirical study on fine-tuning pre-trained large language models for fault diagnosis of complex systems

Zheng, S., Pan, K., Liu, J., & Chen, Y. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110382 · Cited by 127 · see citing works

API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs

Li, M., Zhao, Y., Yu, B., Song, F., Li, H., Yu, H., Li, Z., Huang, F., & Li, Y. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.187 · Cited by 124 · see citing works

Exploring Human-Like Translation Strategy with Large Language Models

He, Z., Liang, T., Jiao, W., Zhang, Z., Yang, Y., Wang, R., Tu, Z., Shi, S., & Wang, X. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00642 · Cited by 124 · see citing works

The benefits, risks and bounds of personalizing the alignment of large language models to individuals

Kirk, H. R., Vidgen, B., Röttger, P., & Hale, S. A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00820-y · Cited by 122 · see citing works

Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference

Chiang, W.-L., Zheng, L., Sheng, Y., Angelopoulos, A. N., Li, T., Li, D., Zhang, H., Zhu, B., Jordan, M., Gonzalez, J. E., et al. (2024) · arXiv · doi:10.48550/arxiv.2403.04132 · Cited by 118

BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions

Hu, W., Xu, Y., Li, Y., Li, W., Chen, Z., & Tu, Z. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.27999 · Cited by 117 · see citing works

UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting

Liu, X., Hu, J., Li, Y., Diao, S., Liang, Y., Hooi, B., & Zimmermann, R. (2024) · Proceedings of the ACM Web Conference 2024 · doi:10.1145/3589334.3645434 · Cited by 115 · see citing works

Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators

Liesenfeld, A., Lopez, A., & Dingemanse, M. (2023) · Proceedings of the 5th International Conference on Conversational User Interfaces · doi:10.1145/3571884.3604316 · Cited by 111 · see citing works

Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text

Mihindukulasooriya, N., Tiwari, S. M., Enguix, C. F., & Lata, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47243-5_14 · Cited by 105 · see citing works

Empowering digital twins with large language models for global temporal feature learning

Sun, Y., Zhang, Q., Bao, J., Lu, Y., & Liu, S. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.02.015 · Cited by 105 · see citing works

Generative AI meets Responsible AI: Practical Challenges and Opportunities

Kenthapadi, K., Lakkaraju, H., & Rajani, N. F. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599557 · Cited by 102 · see citing works

Synthetic Data Generation Using Large Language Models: Advances in Text and Code

Nadas, M. D., Dioşan, L. S., & Tomescu, A. (2025) · IEEE Access · doi:10.1109/access.2025.3589503 · Cited by 99 · see citing works

The Revolution of Multimodal Large Language Models: A Survey

Caffagni, D., Cocchi, F., Barsellotti, L., Moratelli, N., Sarto, S., Baraldi, L., Baraldi, L., Cornia, M., & Cucchiara, R. (2024) · Findings of the Association for Computational Linguistics ACL 2024 · doi:10.18653/v1/2024.findings-acl.807 · Cited by 98 · see citing works

Sniffer: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection

Qi, P., Yan, Z., Hsu, W., & Lee, M. L. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01240 · Cited by 98 · see citing works

A large language model for advanced power dispatch

Cheng, Y., Zhao, H. Q., Zhou, X., Zhao, J., Cao, Y., Yang, C., & Cai, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-91940-x · Cited by 98 · see citing works

LLM Multimodal Traffic Accident Forecasting

de Zarzà, I., de Curtò, J., Roig, G., & Calafate, C. T. (2023) · Sensors · doi:10.3390/s23229225 · Cited by 96 · see citing works

Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application

Yang, C., Zhu, Y. T., Wang, L., Wang, Y., Chen, Q., Gao, C., Yan, B., & Chen, Y. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3699518 · Cited by 93 · see citing works

A Survey on LLM-Based Code Generation for Low-Resource and Domain-Specific Programming Languages

Joel, S., Wu, J. J., & Fard, F. H. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3770084 · Cited by 93 · see citing works

NetGPT: An AI-Native Network Architecture for Provisioning Beyond Personalized Generative Services

Chen, Y., Li, R., Zhao, Z., Peng, C., Wu, J., Hossain, E., & Zhang, H. (2024) · IEEE Network · doi:10.1109/mnet.2024.3376419 · Cited by 93 · see citing works

Large Language Model Instruction Following: A Survey of Progresses and Challenges

Lou, R., Zhang, K., & Yin, W. (2024) · Computational Linguistics · doi:10.1162/coli_a_00523 · Cited by 92 · see citing works

XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters

Zhang, X., & Yang, Q. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615285 · Cited by 87 · see citing works

Large language models meet text-centric multimodal sentiment analysis: a survey

Yang, H., Zhao, Y., Wu, Y., Wang, S., Zheng, T., Zhang, H., Ma, Z., Che, W., Wang, S., Wei, S., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4593-8 · Cited by 88 · see citing works

Self-consuming generative models go MAD

Casco-Rodriguez, J., Alemohammad, S., Luzi, L., Imtiaz, A., Babaei, H., LeJeune, D., Siahkoohi, A., & Baraniuk, R. (2023) · LatinX in AI at Neural Information Processing Systems Conference 2023 · doi:10.52591/lxai202312101 · Cited by 83 · see citing works

InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis

Scaria, K. J., Gupta, H., Goyal, S., Sawant, S., Mishra, S. R., & Baral, C. R. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-short.63 · Cited by 83 · see citing works

Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant

Yuan, M., Bao, P., Yuan, J., Shen, Y., Chen, Z., Xie, Y., Zhao, J., Li, Q., Chen, Y., Zhang, L., et al. (2024) · Medicine Plus · doi:10.1016/j.medp.2024.100030 · Cited by 82 · see citing works

Small language models learn enhanced reasoning skills from medical textbooks

Kim, H., Hwang, H., Lee, J.-W., Park, S., Kim, D. R., Lee, T., Yoon, C., Sohn, J., Park, J., Reykhart, O., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01653-8 · Cited by 82 · see citing works

Knowledge Editing for Large Language Models: A Survey

Wang, S., Zhu, Y., Liu, H., Zheng, Z., Chen, C., & Li, J. (2025) · ACM Computing Surveys · doi:10.1145/3698590 · Cited by 79 · see citing works

LLM-powered Data Augmentation for Enhanced Cross-lingual Performance

Whitehouse, C., Choudhury, M., & Aji, A. F. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.44 · Cited by 79 · see citing works

An HCAI Methodological Framework: Putting it Into Action to Enable Human-Centered AI

Xu, W., Gao, Z., & Dainoff, M. J. (2026) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2025.3631590 · Cited by 75 · see citing works

Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering

Adlakha, V., BehnamGhader, P., Lu, X. H., Meade, N., & Reddy, S. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00667 · Cited by 74 · see citing works

How to Bridge the Gap Between Modalities: Survey on Multimodal Large Language Model

Song, S., Li, X., Li, S., Zhao, S., Yu, J., Ma, J., Mao, X., Zhang, W., & Wang, M. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3527978 · Cited by 74 · see citing works

PathAsst: A Generative Foundation AI Assistant towards Artificial General Intelligence of Pathology

Sun, Y., Zhu, C., Zheng, S., Zhang, K., Sun, L., Shui, Z., Zhang, Y., Li, H., & Yang, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28308 · Cited by 74 · see citing works

GPTCache: An Open-Source Semantic Cache for LLM Applications Enabling Faster Answers and Cost Savings

Bang, F. (2023) · Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023) · doi:10.18653/v1/2023.nlposs-1.24 · Cited by 72 · see citing works

A Review of Large Language Models: Fundamental Architectures, Key Technological Evolutions, Interdisciplinary Technologies Integration, Optimization and Compression Techniques, Applications, and Challenges

Han, S., Wang, M., Zhang, J., Li, D., & Duan, J. (2024) · Electronics · doi:10.3390/electronics13245040 · Cited by 72 · see citing works

Knowledge Graphs for Multi-modal Learning: Survey and Perspective

Chen, Z., Zhang, Y., Fang, Y., Geng, Y., Guo, L., Chen, J., Liu, X., Pan, J. Z., Zhang, N., Chen, H., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103124 · Cited by 72 · see citing works

The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models

Li, J., Chen, J., Ren, R., Cheng, X., Zhao, X., Nie, J.-Y., & Wen, J.-R. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.586 · Cited by 70 · see citing works

AceCoder : An Effective Prompting Technique Specialized in Code Generation

Li, J., Zhao, Y., Li, Y., Li, G., & Jin, Z. G. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3675395 · Cited by 70 · see citing works

Mobile-LLaMA: Instruction Fine-Tuning Open-Source LLM for Network Analysis in 5G Networks

Kan, K. B., Mun, H., Cao, G., & Lee, Y. (2024) · IEEE Network · doi:10.1109/mnet.2024.3421306 · Cited by 68 · see citing works

Black-Box Prompt Optimization: Aligning Large Language Models without Model Training

Cheng, J., Liu, X., Zheng, K., Ke, P., Wang, H., Dong, Y., Tang, J., & Huang, M. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.176 · Cited by 68 · see citing works

LEVA: Using Large Language Models to Enhance Visual Analytics

Zhao, Y., Zhang, Y., Zhang, Y., Zhao, X., Wang, J., Shao, Z., Turkay, Ç., & Chen, S. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3368060 · Cited by 65 · see citing works

Knowledge distillation and dataset distillation of large language models: emerging trends, challenges, and future directions

Fang, L., Yu, X., Cai, J., Chen, Y., Wu, S., Liu, Z., Yang, Z., Lu, H., Gong, X., Liu, Y., et al. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11423-3 · Cited by 64 · see citing works

GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks

Zhang, M., Sun, M., Wang, P., Fan, S., Mo, Y., Xu, X., Liu, H., Yang, C. H., & Shi, C. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645682 · Cited by 61 · see citing works

HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data

Yu, Q., Li, J., Wei, L., Pang, L., Ye, W., Qin, B., Tang, S., Tian, Q., & Zhuang, Y. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01230 · Cited by 59 · see citing works

HPC-GPT: Integrating Large Language Model for High-Performance Computing

Ding, X., Chen, L., Emani, M. K., Liao, C., Lin, P., Vanderbruggen, T., Xie, Z., Cerpa, A., & Du, W. (2023) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3624062.3624172 · Cited by 58 · see citing works

Large Language Models for Text Classification: From Zero-Shot Learning to Instruction-Tuning

Chae, Y., & Davidson, T. (2025) · Sociological Methods & Research · doi:10.1177/00491241251325243 · Cited by 57 · see citing works

Lion: Adversarial Distillation of Proprietary Large Language Models

Jiang, Y., Chan, C., Chen, M., & Wang, W. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.189 · Cited by 55 · see citing works

Prompting Frameworks for Large Language Models: A Survey

Liu, X., Wang, J., Yuan, X., Sun, J., Dong, G., Di, P., Wang, W., & Wang, D. (2026) · ACM Computing Surveys · doi:10.1145/3789253 · Cited by 55 · see citing works

Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Yang, F., Zhao, P., Wang, Z., Wang, L., Qiao, B., Zhang, J., Garg, M., Lin, Q., Rajmohan, S., & Zhang, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-industry.29 · Cited by 54 · see citing works

FashionGPT: LLM instruction fine-tuning with multiple LoRA-adapter fusion

Gao, D., Ma, Y., Liu, S., Song, M., Jin, L., Jiang, W. R., Wang, X., Ning, W., Yu, S., Xuan, Q., et al. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112043 · Cited by 53 · see citing works

DesignQA: A Multimodal Benchmark for Evaluating Large Language Models’ Understanding of Engineering Documentation

Doris, A. C., Grandi, D., Tomich, R., Alam, M. F., Ataei, M., Cheong, H., & Ahmed, F. (2024) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4067333 · Cited by 53 · see citing works

GeoTool-GPT: a trainable method for facilitating Large Language Models to master GIS tools

Cheng, W., Zhang, Y., Zhao, X., Zeng, Z., Wang, Z., Lin, J., Guan, Q., & Yu, W. (2024) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2024.2438937 · Cited by 53 · see citing works

Unlearn What You Want to Forget: Efficient Unlearning for LLMs

Chen, J., & Yang, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.738 · Cited by 52 · see citing works

Self-Alignment with Instruction BacktranslationPreprint

Li, X., Yu, P., Zhou, C., Schick, T., Levy, O., Zettlemoyer, L., Weston, J., & Lewis, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.06259 · Cited by 94 · see citing works

Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages

Huang, S.-C., Li, P.-Z., Hsu, Y.-C., Chen, K.-M., Lin, Y. T., Hsiao, S.-K., Tsai, R. T., & Lee, H.-Y. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.590 · Cited by 15 · see citing works

IHEval: Evaluating Language Models on Following the Instruction Hierarchy

Zhang, Z., Li, S., Zhang, Z., Liu, X., Jiang, H., Tang, X., Gao, Y., Li, Z., Wang, H., Tan, Z., et al. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.425 · Cited by 22 · see citing works

Unified Pragmatic Models for Generating and Following Instructions

Fried, D., Andreas, J., & Klein, D. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1177 · Cited by 64 · see citing works

Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering

Si, S., Zhao, H., Chen, G., Gao, C., Bai, Y., Wang, Z., An, K., Luo, K., Chen, Q., Qi, F., et al. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.804 · Cited by 11 · see citing works