Cited by 1,243Topic ModelingNatural Language Processing TechniquesMultimodal Machine Learning Applications

The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.

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

Open access

Wang, Yizhong, Kordi, Yeganeh, Mishra, Swaroop, Liu, Alisa, Smith, Noah A., Khashabi, Daniel, Hajishirzi, Hannaneh·2023·Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

DOI 10.18653/v1/2023.acl-long.754

Open this paper in CoChat — free account Open PDF

Abstract

Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.18653/v1/2023.acl-long.754

Cites

54 of 54 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
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
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
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
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
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
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
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
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

Show all 39 references

Research works printed but not linked: 3

Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. In Advances in Neural Information Processing Systems (NeurIPS) Workshop on Energy Efficient Machine Learning and Cognitive Computing.
Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Tr- ishala Neeraj, Jos Rozen, Abheesht Sharma, An- drea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M Rush. 2022. Multi- task Prompted Training Enables Zero-Shot Task Gen- eralization. In International Conference on Learning Representations (ICLR).
Eric Zelikman, Jesse Mu, Noah D Goodman, and Yuhuai Tony Wu. 2022. STar: Self-taught reasoner bootstrapping reasoning with reasoning. In Advances in Neural Information Processing Systems (NeurIPS).

Sorted by citations

Items 901–1,000 of 1,036

AI-Driven Mental Healthcare 5.0: A Survey of Opportunities and Challenges in Leveraging Large Language Models and Generative AIPreprint
Kumar, A., Gupta, K., Verma, K., & Kumar, S. (2025) · doi:10.22541/au.174291179.92552739/v1
Enhancing Radiology Report Interpretation with Large-Scale Language Models: A Two-Stage Fine-Tuning Approach
Pi, S.-W., Park, M., & Lee, B.-D. (2025) · International Conference on Advanced Communications Technology (ICACT) · doi:10.23919/icact63878.2025.10936299
Agent-Based ChatGPT for Multi-Party Conversation Task Processing
万, 静. (2025) · Software Engineering and Applications · doi:10.12677/sea.2025.142022
Cognitive Bias and Reassignment: Who Can Contribute High Quality LLM Data
Gao, Y., Xiong, Y., Hu, Z., Zhang, Y., Wang, M., & Wang, H. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i27.35018
Evaluating Verbs of Chatting in Large Language Models
Zhou, J., & Wang, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-4407-0_8
Construction and validation evaluation of a high-quality dataset in the national defense mobilization domain based on large language models
Shen, J., Cui, X., Gao, Z., & Zhang, M. (2025) · International Conference on Computer Application and Information Security (ICCAIS) · doi:10.1117/12.3061094
IndoWebGen: Language Model untuk Pembuatan Website secara Otomatis Berdasarkan Instruksi Bahasa Indonesia melalui Metode LoRA
Wicaksono, A. T., & Jati, H. (2025) · Journal of Information Engineering and Technology (JIETY) · doi:10.21831/jiety.v2i2.931
Advancing sentiment understanding and safety in NLP systems
Deng, Y. (2025) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/183610
Making language models better human-like learners
Qin, C. (2025) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/183806
Bypassing LLM Safeguards: The In-Context Tense Attack Approach
Wang, S., Geng, R., Lei, S., Lv, Y., & Zhang, H. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-4245-8_23
A Survey of LLM-Based Methods for Synthetic Data Generation and the Rise of Agentic Workflows
Alismail, A., & Lanquillon, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93418-6_9 · Cited by 3
COTKEG: A Chain-of-Thought and Knowledge Graph Enhanced Method for Medical Large Language Models
Huang, J., & Yan, E.-L. (2025) · International Symposium on Computer Applications and Information Systems (ISCAIS) · doi:10.1109/iscait64916.2025.11010691
SWITCH: Studying with Teacher for Knowledge Distillation of Large Language Models
Koo, J., Hwang, Y., Kim, Y., Kang, T., Bae, H., & Jung, K. (2025) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2025.findings-naacl.206 · Cited by 6
Towards Unified, Dynamic and Annotation-based Visualisations and Exploration of Annotated Big Data Corpora with the Help of Unified Corpus Explorer
Bönisch, K., Abrami, G., & Mehler, A. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-demo.42
Prompto: An open source library for asynchronous querying of LLM endpoints
Chan, R. S.-Y., Nanni, F., Williams, A. R., Brown, E., Burke-Moore, L., Chapman, E., Onslow, K., Sippy, T., Bright, J., & Gabašová, E. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-demo.11
AI-Assisted Legal Education for Student Grouping Using Large Language Models
Miao, J., Wu, Y., & Guo, X. (2024) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait63902.2024.11022064
Conditional Information-Based Adaptive Fusion Local Redrawing Model with Data Extension Framework
Zhou, N. (2025) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit65432.2025.11035819
Progressive Fusion of LLM-Augmented Multi-Modal Data for Enhanced Product Recommendations
Li, X., Gong, L., Liu, X., & Xiang, X. (2025) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit65432.2025.11035374
A multimodal traffic scene understanding model integrated with optical flow maps
Wang, Y., Zhou, Y., Zhong, M., Mei, Y., Fujita, H., & Aljuaid, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130838
Self-citation
Intelligent assistant in radiation protection based on large language model with knowledge base
Hu, A., Li, K., Wu, Z., Zhang, H., Qiu, R., & Li, J. (2025) · Radiation and Environmental Biophysics · doi:10.1007/s00411-025-01124-4
SoTaNa: An Open-Source Software Engineering Instruction-Tuned Model
Shi, E., Wang, Y., Zhang, F., Chen, B., Zhang, H., Wang, Y., Guo, D., Du, L., Shi, H., Zhang, D., et al. (2025) · IEEE/ACM International Conference on AI Foundation Models and Software Engineering (FORGE) · doi:10.1109/forge66646.2025.00010
Self-citation
Examine the Opportunities and Challenges of Large Language Model (LLM) For Indic Languages
Panchal, B. Y. (2024) · Journal of Information Systems Engineering & Management · doi:10.52783/jisem.v9i4s.11887
ПРЕОБРАЗОВАНИЕ СЫРЫХ ТЕКСТОВЫХ ДАННЫХ В ИНСТРУКЦИОННЫЙ ДАТАСЕТ ДЛЯ ДООБУЧЕНИЯ МОДЕЛИ-ГЕНЕРАТОРА. ПРЕДНАЗНАЧЕННОЙ ДЛЯ РАБОТЫ В СИСТЕМЕ RAG
Елисеев, В. О., Максимова, А. Ю., & Иванович, Б. В. (2025) · System Informatics · doi:10.31144/si.2307-6410.2025.n27.p77-92
From Textbooks to Data: Generating High-Quality Domain-Specific QA Data with Large Language Models
Huang, X., Hu, W., Cui, Z., & Xiao, Z. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9994-0_34
Exploring Synthetic Data Generation Methods
Kumar, A. R., Ravikumar, G. K., Sathisha, G., & Chethan, H. V. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-4430-8_36
ScoreCLIQ: A Dynamic LLM-Based Framework for Item Difficulty Estimation
Sarkar, S., Ravikiran, M., & Saluja, R. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-99264-3_21
Tree-of-Evolution: Tree-Structured Instruction Evolution for Code Generation in Large Language Models
Luo, Z., Li, K., Lin, H., Tian, Y., Kankanhalli, M., & Ma, J. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.14
FINKRX: Establishing Best Practices for Korean Financial NLP
Son, G., Ko, H., Jung, H., & Hwang, C. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-industry.81
Enhanced Data Synthesis for LLM through Reasoning Structures Generated by Hierarchical GFlowNet
Bu, T., Zhang, M., Duan, H., Li, S., Hu, L., & Li, Y. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.821 · Cited by 1
ALPS: Attention Localization and Pruning Strategy for Efficient Adaptation of Large Language Models
Chen, H., Li, H., Xiao, Z., Gao, L., Zhang, Q., Hu, X., Wang, N., Fu, X., & Zhao, J. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.612
Bridge-Coder: Transferring Model Capabilities from High-Resource to Low-Resource Programming Language
Zhang, J., Zhang, J., Li, Y., Pi, R., Pan, R., Liu, R., Zheng, Z., & Zhang, T. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.567 · Cited by 1
Tag-Evol: Achieving Efficient Instruction Evolving via Tag Injection
Wang, Y., Zhou, S., Guo, C., & Zhu, Q. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.409
Self-citation
Align2LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction Curation
Huang, H., Liu, J., Yu, Z., Cai, L., Jiao, D., Zhang, W., Tang, S., Li, J., Jiang, H., Li, H., et al. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.458
Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM)
(2025) · doi:10.18653/v1/2025.knowllm-1
One QuantLLM for ALL: Fine-tuning Quantized LLMs Once for Efficient Deployments
Yi, K., Xu, Y., Chang, H., Meng, Y., Zhang, T., & Li, J. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.1124 · Cited by 2
From Real to Synthetic: Synthesizing Millions of Diversified and Complicated User Instructions with Attributed Grounding
Zhu, C., Xu, B., Wang, X., & Mao, Z. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.517 · Cited by 3
Think, Prune, Train: Can Small Models Teach Themselves to Reason?
Costello, C., Guo, S., Goldie, A., & Mirhoseini, A. (2025) · IEEE International Conference on LLM-Aided Design (ICLAD) · doi:10.1109/iclad65226.2025.00012
A Multi-Document Summarization Method for Customer Feedback Based on Large Language Models
Bao, S., Guan, X., Cao, B., & Yang, C. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-93251-9_15
A Comprehensive Study of Large Language Models
Kumar, P., Chaudhary, A., Sahu, S., Jain, M. K., & Jaiswal, U. K. (2025) · Wiley eBooks · doi:10.1002/9781394287499.ch2
Inspire Me with Your Questions: Repurposing Historical Questions for New Documents
Liu, Y., Cao, Y., & Luo, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02088-8_6
Emis: Scaling Mathematical Reasoning With Evolutionary Instruction Synthesis
Luo, H., Deng, W., Tian, Z. F., Zhou, N., Tang, Y., & Liang, X. (2025) · IEEE China Conference on System Simulation Technology and its Applications (CCSSTA) · doi:10.1109/ieeeconf65522.2025.11137079
Advancing unsupervised domain adaptation and context-augmented learning for scalable AI systems
Long, Q. (2025) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/201670
Unpacking The Influence of Coaching Style Leadership on Startup Performance: The Serial Mediation of Entrepreneurial Passion and Entrepreneurial Satisfaction
Rafiq, M., Rehman, Z. U., Arzoo, H., Tanveer, M., & Akhtar, N. (2025) · Journal of Social & Organizational Matters · doi:10.56976/jsom.v4i3.295
Balanced KGQA Through Prompt-Based Data Augmentation with LLMs
Ren, X., & Wang, Z. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-94892-3_33
EAVIT: Efficient and Accurate Human Value Identification From Text Data via LLMs
Zhu, W., Xie, Y., Song, G., & Zhang, X. (2025) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2025/934
The Impacts of Artificial Intelligence Technologies on Consumer Well-being and Quality of Life: A Current Literature Review
Durmaz, Y. (2025) · Brain and Neurological Disorders · doi:10.31579/2642-973x/139
Compact and efficient language modeling for classical poetry: generation and interpretation
Cao, D., Cheng, C., & Zhang, G. (2025) · Multimedia Systems · doi:10.1007/s00530-025-01988-y · Cited by 1
From unimodal to multimodal: a framework for generating high-quality multimodal emotional chit-chat dialogue
Yang, H.-T., Zheng, T., Zhao, Y., Wu, Y. J., Yuan, J., Che, W., Wang, S., Wei, S., & Qin, B. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4591-x
Hybrid CNN–GRU attention network for predicting asthma incidence in urban environments
Hà, V. T., Thanh, P. V., & Khanh, L. A. (2025) · Edelweiss Applied Science and Technology · doi:10.55214/2576-8484.v9i10.10389
Automated Question-Answer Pair Generation for Continuous Documentation in Chinese Domains
Duan, J., Wang, M., Wu, Y., Yang, Y., Su, D., & Lin, Q. (2025) · International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA) · doi:10.1109/caibda65784.2025.11183001
Towards Reliable Statistical Guarantees for LLM Alignment EvaluationPreprint
Jin, G., Lin, Y., Liu, S., Du, Y., Huang, T., Hu, L., Wang, D., & Huang, X. (2025) · doi:10.36227/techrxiv.176053298.85586285/v1
Chain-of-Thought Augmented Fine‑Tuning of a Distilled Llama‑8B Model for SIEM Detection Query Generation
Radah, T., Chaoui, H., & Saadi, C. (2025) · Ingénierie des systèmes d information · doi:10.18280/isi.300810
Reducing Hallucination in Multilingual Voice Agents Using Instruction-Tuned Models
Safia, M. A. M. (2024) · Computer Fraud & Security · doi:10.52710/cfs.773
ADQ-DS: An Adaptive Diversity and Quality-Cooperative Data Selection Method for Instruction Tuning
Xie, M., Tao, Z., & Zhang, Z. (2025) · International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC) · doi:10.1109/aiotc66747.2025.11198656
Ensuring Responses Contain Appropriate Images: Timing Judgment for Multimodal Responses
Yang, H., Zheng, T., Zhao, Y., & Qin, B. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3758178
Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts
Chen, X., Fang, Y., Li, J., Xiao, Q., Lin, J., Tang, S., & Zhuang, Y. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3758288
BOLT: Building Open-Source LLMs for Your Target Domain via Automated Hierarchical Knowledge Distillation
Lu, R., Fan, Z., Wang, G., & Shi, Q. (2025) · Applied Sciences · doi:10.3390/app152111393
An Improved Autoregressive Evaluation Paradigm for Large Language Models
Zhang, J., Pan, R., Hu, Y., Shum, K., Yao, G., Liu, X., Pi, R., Dong, H., Diao, S., Lin, Y., et al. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3763000
A Process for Developing a Traffic Safety Domain Specific Language Model Using Fine-Tuning and RAG
Lee, Y., Kim, Y., Moon, S., Joo, S., Lee, J., & Yun, I. (2025) · The Journal of The Korea Institute of Intelligent Transport Systems · doi:10.12815/kits.2025.24.5.54
Unlocking the Potential of Smaller Language Models as Superior Instruction Evolvers
Hui, T., Zhao, L., Dong, G., Zhang, Y., & Su, S. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761290
In2X at WMT25 Translation Task
Pang, L., Mao, H., Xiao, Q., Ruihan, C., Zhang, J., Liu, H., & Li, X. (2025) · Conference on Machine Translation (WMT) · doi:10.18653/v1/2025.wmt-1.43
ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM
Wang, L., Duan, C., Zhao, P., Yang, F., Shi, Y., Luo, X., Xu, B., Deng, W., Lin, Q., & Zhang, D. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.1273
GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning
Gabouj, O., Charaf, K., Zakazov, I., Baldwin, N., & West, R. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.1047
PROOD: A Simple LLM Out-of-Distribution Guardrail Leveraging Response Semantics
Tint, J. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.1272
Staged Knowledge Distillation Through Least-to-Most Prompting: Optimizing Teacher Guidance via Difficulty-Aware Training
Zhang, M., & Liu, L. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.451
Curr-ReFT: Overcoming Training Bottlenecks in Small-scale Vision-Language Models via Curriculum Reinforcement Finetuning
Deng, H., Zou, D., Zhao, X., Ma, R., Guo, Y., Cao, Y., & Kang, Y. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.643
Mixture of Weight-shared Heterogeneous Group Attention Experts for Dynamic Token-wise KV Optimization
Song, G., Liao, D., Zhao, Y., Ye, K., Xu, C.-Z., & Gao, X. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.1166 · Cited by 1
Toward Optimal LLM Alignments Using Two-Player Games
Zheng, R., Guo, H., Liu, Z., Zhang, X., Yao, Y., Xu, X., Wang, Z., Xi, Z., Gui, T., Zhang, Q., et al. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.6
ExpertGenQA: Open-ended QA generation in Specialized Domains
Shahgir, H. S., Lim, C., Chen, J., Papalexakis, E. E., & Dong, Y. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.159
MAIN: Mutual Alignment Is Necessary for instruction tuning
Yang, F., Liu, J., Zhang, X., Liu, H., Cao, X., Zhan, Y., Sun, H., Deng, W., Sun, F., & Zhang, Q. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.644
Agentic-R1: Distilled Dual-Strategy Reasoning
Du, W., Aggarwal, P., Welleck, S., & Yang, Y. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.604 · Cited by 4
CYCLE-INSTRUCT: Fully Seed-Free Instruction Tuning via Dual Self-Training and Cycle Consistency
Shen, Z., Chen, H., Tang, Y., Zhu, S., Wentao, Y., Hu, X., Wang, H., Chen, G., & Zhao, J. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.258 · Cited by 1
Judge and Improve: Towards a Better Reasoning of Knowledge Graphs with Large Language Models
Zhiqiang, M., Yang, H., Li, J., Liu, Y., Wong, S., & Huang, J. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.269
CSEQL-Llama: A Text-to-CSEQL Framework Based on Multi-agent Collaborative Data Synthesis and Parameter-Efficient Fine-Tuning
Li, Y., Peng, J., Yang, L., Li, Y., Shi, F., Xue, P., & Xu, C. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4088-4_7
Dynamic Multi-views In-Context Learning with Large Language Models for Aspect-Based Sentiment Analysis
Wang, Y., Zhang, Z., Shan, L., & Liu, B. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3349-7_45
Self-citation
ZeroSeedRec: Enhancing Zero-Shot Recommendation with Domain-Specific Instruction-Tuned LLMs: A Pipeline for Synthetic Seed and Self-Instruct Data Generation
Elbella, E., Aly, A., & Kashef, R. F. (2025) · Novel Intelligent and Leading Emerging Sciences Conference (NILES) · doi:10.1109/niles68063.2025.11231926
Dynamic Knowledge Elicitation: Leveraging Student Feedback for Improved Language Model Distillation
Muller, R., Xie, Y., Le, L., & Wu, S. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227292
LentEx: Generalizable Latent Entity Extraction via Synthetic Data and Instruction-Tuned LLMs
Bodhwani, U., Ling, Y., Senthilkumar, C. C., Dong, S., Feng, Y., Li, H., & Goyal, A. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228382
PromptMetric: Prompt Recipe as an Automatic Metric for Evaluating Open-domain Question Answering Systems
Wang, P., Zeng, X., Zhu, C., Xu, B., Mao, Z., & Zhang, Y. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228172
SynthCoder: Anti-pattern identification and model training for FIM mode code completion
Yu, D., Yan, X., Li, Z., Xiao, J., He, H., Yu, Y., Zhang, H., Rong, G., & Huang, X. (2026) · Empirical Software Engineering · doi:10.1007/s10664-026-10957-6 · Cited by 2
Are You There God? Lightweight Narrative Annotation of Christian Fiction with LMs
Hicke, R. M. M., Haggard, B. W., Ferrante, M., Khanna, R., & Mimno, D. (2025) · doi:10.63744/u7smpke7ajs1
Efficient Long Chain-of-Thought Elicitation through Synthetic Data Generation and Targeted Fine-TuningPreprint
Abdulloh, A. (2025) · doi:10.36227/techrxiv.176369747.70179230/v1
MQA-SQL: Mitigating Question Ambiguity in Text-to-SQL with Multi-model Collaboration and Multi-variant Query Rephrasing
Huang, Y., Guo, J., Zeng, J., Gao, C., Han, P., & Liu, C. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3346-6_22
WizardEvent: Empowering Event Reasoning by Hybrid Event-Aware Data Synthesizing
Tao, Z., Chen, X., Jin, Z., Bai, X., Zhao, H., Hu, W., Tao, C., & Ma, S. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3634839
The AI Archipelago: Emergent Cultural Coordination in Networked AI SystemsPreprint
Mooy, M. D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5800342
Think-to-Detect: Rationale-Driven Vision–Language Anomaly Detection
Abdalla, M. I., Kasem, M. S., Mahmoud, M., Senussi, M. F., Abdallah, A., & Kang, H. (2025) · Mathematics · doi:10.3390/math13243920
DEFT: Data-Efficient Fine-Tuning Through Multi-Dimensional Data Selection
Dai, S., Liu, X., & Yu, Y. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3642562
DiverSeed: Integrating Active Learning for Target Domain Data Generation in Instruction Tuning
Gao, J., Qi, M., Gao, X., Xiang, S., Ji, K., Ruan, J., Liu, T., & Fu, Y. (2025) · Machine Learning · doi:10.1007/s10994-025-06926-7
A Retrospective and Adaptive Framework to Improve LLMs
Chang, E. Y. (2025) · ACM eBooks · doi:10.1145/3749421.3749439
Fine-tuning Llama3 for Integrated Analysis of Network Packet and System Log Data
Choi, H.-M., & Lee, Y. (2025) · Asia-Pacific Conference on Communications (APCC) · doi:10.23919/apcc64555.2025.11279921
Error-Guided Multimodal Sample Selection with Hallucination Suppression for LVLMs
Cheng, H., Shang, L., Chen, X., Feng, T., & Zhang⋆, Y. (2025) · Computers · doi:10.3390/computers14120564
Development of Multilingual AI Legal Assistants for Real-Time Legal Aid Delivery, Document Drafting, and Procedural Guidance in Underserved Judicial Systems
Adain, A. A., Alasadi, A. Y., Hameed, M., Mehd, R., Hamza, A. A., Hameed, D. W., & Mustafa, A. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11292048
Comparison of representative demonstration designing methods of Language Models
Tiwari, S. (2023) · Open Research Knowledge Graph (ORKG) · doi:10.48366/r598308
SDIDM: An agricultural irrigation decision model based on Chain-of-Thought Prompts enhancement
Dong, W., Liu, Y., Zhang, Q., Jia, L., Kong, B., & Liu, S. (2025) · Data Intelligence · doi:10.3724/2096-7004.di.2025.0142
Domain Specialization for Complex Multi-domain Problems: A Comparison of Large Language Model-Based Methods
Beliakin, E., & Sagirova, A. (2025) · Studies in computational intelligence · doi:10.1007/978-3-032-07690-8_8
Bootstrapping Post-Training for LLM Translator
Gao, W., Li, Y., Min, Q., & Zhang, Y. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3624962
Collaborative Co-Design Practices for Supporting Synthetic Data Generation in Large Language Models: A Pilot Study
Candello, H., Horesh, R., Adebiyi, A., Azmat, M., de Paula, R. A., & Chiazor, L. (2025) · Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP) · doi:10.18653/v1/2025.hcinlp-1.11
Cognitive Feedback: Decoding Human Feedback from Cognitive Signals
Harada, Y., & Oseki, Y. (2025) · Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP) · doi:10.18653/v1/2025.hcinlp-1.17
Are Task-Specific Datasets the Be-All and End-All for LLM Performance?
Yang, J., Tan, Y., Hu, W., Zhou, J., Yang, Z., Luo, Z., & Luo, W. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227270
VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-Reflection
Wang, Q., Gao, X., He, Y., Han, J., Li, J., Dong, S., Ma, Z., & Gong, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37592-6_30

Authors: Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., & Hajishirzi, H.
Affiliations: Johns Hopkins University (US)
Corresponding author: Wang, Yizhong
ORCID: Kordi, Yeganeh
Mishra, Swaroop
Liu, Alisa
Smith, Noah A.
Khashabi, Daniel
Hajishirzi, Hannaneh
Venue: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher: Association for Computational Linguistics
Published: 2023
Pages: 13484-13508
ISSN: 0736-587X (ISSN-L)
DOI: 10.18653/v1/2023.acl-long.754
OpenAlex: W4385572634
Cited by: 1,243
References: 39
Citations by year: 2026: 317 · 2025: 435 · 2024: 345 · 2023: 145
FWCI: 196.73
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University) (cc-by)
Funders: Defense Advanced Research Projects Agency (N66001-19-2-4031, N66001-19)
Multidisciplinary University Research Initiative
Office of Naval Research (N00014‐18‐1‐2670, N00014-18-1-2826, MURI N00014-18-1-2670, N00014)
Naval Information Warfare Center Pacific
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Keywords: self-education, language model alignment, instruction tuning, self-supervised learning, large language models

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks