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

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

PromptLab: A Collaborative Platform for Prompt Engineering and Dataset Curation

Al-shaibani, M. S., Alyafeai, Z., Refai, D., Alomari, N., Ashraf, A., Alheraki, M., Alturki, M., Luqman, H., & Ahmad, I. (2026) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2026.eacl-demo.18 · Cited by 1

Scale, Structure, and Stability: When Does LLM-Based Data Augmentation Improve Temporal Robustness in Web Intrusion Detection?

Lee, J. Y., & Kim, H. (2026) · Electronics · doi:10.3390/electronics15071344 · Cited by 1

C2|Q>: A Robust Framework for Bridging Classical and Quantum Software Development

Ye, B., Khan, A. A., Pihkakoski, T., Liang, P., Akbar, M. A., Silveri, M., & Malmi, L. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3803018 · Cited by 1

Technical and Ethical Perspectives on Building Intelligent Legal Assistants With LLM and RAG Pipelines

Mbounkap, J., Djigo, H., Masheh, T., Nzooh, A., & Kar, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7685-1.ch013 · Cited by 1

Parameter-Efficient Fine-Tuning of LLaMA 2 for the Kazakh Language: Advancing Low-Resource Language Models

Mussa, A., & Мансурова, М. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6935-6_42 · Cited by 1

Generation of Programmatic Rules for Document Forgery Detection Using Large Language Models

Schmidberger, V., Eberhardinger, M., Maghsudi, S., & Maucher, J. (2025) · International Conference on Machine Learning and Applications · doi:10.1109/icmla66185.2025.00225 · Cited by 1

Toward Scalable ASL Education: Egocentric Stereo Sensing with LLM Feedback for Error-Aware Learning

Cai, Y., Li, Z., Lu, T., Zhu, Y., Wu, Y.-S., Zhang, Q., Xu, X., Jin, Z. K., Gowda, M. K., & Jin, Y. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790774 · Cited by 2

Generative AI as an Information Intermediary: A Novel Deep Learning Method for Financial Distress Prediction

Wang, Z., Wu, C., Jiang, C., & Zhao, H. (2026) · MIS Quarterly · doi:10.25300/misq/2026/19476 · Cited by 1

Measuring the gap: correlating synthetic-to-real drift with PHI de-identification performance

Cornelius, J., & Rinaldi, F. (2026) · Genomics & Informatics · doi:10.1186/s44342-026-00072-9 · Cited by 1

Human–AI Interaction in LLM

Zakershahrak, M. (2026) · Springer eBooks · doi:10.1007/978-981-95-3658-0_72 · Cited by 2

Classifying crash severity by SAE automation level using transformer models: Insights for automated vehicle policy

Mimi, M. S., Islam, M. M., Barua, S., Chowdhury, T. I., Tusti, A. G., Chhetri, G., & Das, S. (2026) · Transport Policy · doi:10.1016/j.tranpol.2026.104222 · Cited by 1

Key Technologies of AI Large Language Models in the Field of Traditional Chinese Medicine

Fei, H., Yanhui, L., Junxia, L., Chen, Z., Weirong, X., & Zhang, L. (2026) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001426590263 · Cited by 1

Solver-Independent Automated Problem Formulation via LLMs for High-Cost Simulation-Driven Design

Li, Y., Wang, H., Xue, B., Zhang, M., & Jin, Y. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.102 · Cited by 3

ClassEval-Pro: A Cross-Domain Benchmark for Class-Level Code Generation

Chen, Y., Xie, C., Shi, Y., Zeng, W., Wang, Y., Zhang, H., & Gu, X. (2026) · ACM International Conference on AI-Powered Software (AIware) · doi:10.1145/3805760.3814925 · Cited by 3

Naija-Petro AI: Adapting Large Language Models (LLMs) for Oil and Gas Applications Through Domain-Specific Fine-Tuning

Ashinze, E. C. (2026) · SPE Nigeria Annual International Conference and Exhibition · doi:10.2118/234918-ms · Cited by 1

Communicative Action in the Age of Artificial Intelligence: Theorizing LLMs as Communicative and Strategic Agents

Hila, A. A. (2026) · Philosophy & Technology · doi:10.1007/s13347-026-01168-4 · Cited by 1

GeoTP: parameter-level intelligent planner of GIS tool-use chains based on instruction tuning of large language models

Tang, J., Li, Z., Yang, J., 林雕, Zhang, B., Yang, G., & Fang, L. (2026) · The Knowledge Engineering Review · doi:10.48130/ker-0026-0013 · Cited by 1

Generative AI Technologies, Techniques and Tensions: A Primer

Behrens, J. T. (2026) · Springer eBooks · doi:10.1007/978-3-032-24819-0_2 · Cited by 2

Unlocking Foundation Models for Privacy-Enhancing Speech Understanding: An Early Study on Low Resource Speech Training Leveraging Label-guided Synthetic Speech Content

Feng, T., Bose, D., Shi, X., & Narayanan, S. S. (2023) · Symposium on Security and Privacy in Speech Communication (SPSC) · doi:10.21437/spsc.2023-2 · Cited by 1

Ensemble-Instruct: Instruction Tuning Data Generation with a Heterogeneous Mixture of LMs

Lee, Y., Sultan, M. A., El-Kurdi, Y., Naseem, T., Munawar, A., Florian, R., Roukos, S., & Astudillo, R. F. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.836 · Cited by 3

Work in Progress: ChatGPT as an Assistant in Paper Writing

Baizán, P., Gil, R., García-Loro, F., Pérez, C., Sancristobal, E., & Castro, M. A. (2023) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie58773.2023.10343230 · Cited by 2

Building a Medical Q&A System Based on Deep Learning and Knowledge Graphs

Wang, Z. (2024) · International Conference on Public Management and Intelligent Society (PMIS) · doi:10.4108/eai.15-3-2024.2346400 · Cited by 1

Cybernetic Sentinels: Unveiling the Impact of Safety Data Selection on Model Security in Supervised Fine-Tuning

Wang, R., He, H., & Sun, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5669-8_23 · Cited by 1

CMMQC: Cascaded Multi-Model Quality Control for Unsupervised Data-to-Text Generation

Zhang, W., Sun, X., Luo, Y., Gao, W., & Zhu, Y. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650876 · Cited by 1

The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning

Xie, W., Li, J., Mu, Y., Zhang, H., Zhao, S., & Zheng, X. (2024) · International Conference on Asian Language Processing · doi:10.1109/ialp63756.2024.10661174 · Cited by 1

TRINS: Towards Multimodal Language Models that Can Read

Zhang, R., Zhang, Y., Chen, J., Zhou, Y., Gu, J., Chen, C., & Sun, T. (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/cvpr52733.2024.02131 · Cited by 1

Unleashing the Potential of Compact Language Models: A Context-Optimized Soft Prompting Approach

Jiang, Z. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/97/20241352 · Cited by 1

Evolutionary Contrastive Distillation for Language Model Alignment

Katz-Samuels, J., Li, Z., Yun, H., Nigam, P., Xu, Y., Petricek, V., Yin, B., & Chilimbi, T. M. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.307 · Cited by 3

AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation

Luo, Z., Li, X., Lin, H., Ma, J., & Bing, L. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.66 · Cited by 1

Free your mouse! Command Large Language Models to Generate Code to Format Word Documents

Rao, S., Li, L., Liu, J., Weixin, G., Gao, X., Lim, B., & Ma, C. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.902 · Cited by 1

TIFD: Tibetan Instruction-Following Dataset for Large Language Models Supervised Fine-Tuning

Zhuang, W., Cairen, D., & Sun, Y. (2024) · Data Intelligence · doi:10.3724/2096-7004.di.2024.0010 · Cited by 1

EVOLUTION AND DISTRIBUTION ANALYSIS OF MULTIMODAL ARTIFICIAL INTELLIGENCE SYSTEMS

Kapiton, A., Tyshсhenko, D., Desiatko, A., & Lazorenko, V. (2024) · Системи управління навігації та зв’язку Збірник наукових праць · doi:10.26906/sunz.2024.4.075 · Cited by 1

ASLoRA: Adaptive Sharing Low-Rank Adaptation Across Layers

Hu, J., Xue, J., Zhang, M., Chen, Y., Ren, Z., Wu, S., Chen, Z., & Ren, P. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114072 · Cited by 4

The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning

Xie, W., Li, J., Mu, Y., Zhang, H., Zhao, S., Zheng, X., & Song, K. (2025) · International Journal of Asian Language Processing · doi:10.1142/s2717554524500176 · Cited by 1

Investigating ChatGPT Translation Hallucination from an Embodied-Cognitive Translatology Perspective

Jiao, H., Li, X., Ding, J., & Xiao-jun, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-2885-8_12 · Cited by 1

Instruction Tuning for Developing Large Language Models Specialized in Chemical Domain

Lee, J., Kim, H. Y., Lee, S., Kim, Y., Lee, K. S., & Kim, S.-B. (2025) · Journal of Korean Institute of Industrial Engineers · doi:10.7232/jkiie.2025.51.2.150 · Cited by 1

Moral Judgment with a Large Language Model-Based Agent

Xiong, S., Gu, H., Liang, W., & Yin, L. (2025) · Electronics · doi:10.3390/electronics14132580 · Cited by 1

Beyond the Benchmark: A Customizable Platform for Real-Time, Preference-Driven LLM Evaluation

Zografos, G., & Moussiades, L. (2025) · Electronics · doi:10.3390/electronics14132577 · Cited by 2

Implementation of Technology-Based Learning Strategies through Digital Storytelling Applications of Elementary School Students

Putri, W., Ardianti, I., & Wirandani, W. (2025) · Aphorisme Journal of Arabic Language Literature and Education · doi:10.37680/aphorisme.v6i1.6871 · Cited by 1

Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues

Fichtel, L., Spliethöver, M., Hüllermeier, E., Jimenez, P., Klowait, N. O., Kopp, S., Ngomo, A.-C. N., Robrecht, A. S., Scharlau, I., Terfloth, L., et al. (2025) · doi:10.64136/safy2489 · Cited by 1

Reveal-Bangla: A Dataset for Cross-Lingual Multi-Step Reasoning Evaluation

Islam, K. I., & Sarti, G. (2025) · Workshop on Bangla Language Processing (BLP) · doi:10.18653/v1/2025.banglalp-1.3 · Cited by 1

Towards Human-Like Virtual Beings: Simulating Human Behavior in 3D Scenes

Liang, C., Wang, W., & Yang, Y. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01001 · Cited by 1

Indicative Summarization of Long Discussions

Syed, S., Schwabe, D., Al‐Khatib, K., & Potthast, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.166 · Cited by 2

TaskWeb: Selecting Better Source Tasks for Multi-task NLP

Kim, J., Asai, A., Ilharco, G., & Hajishirzi, H. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.680 · Cited by 5

Here We Go Again: Modern GEC Models Need Help with Spelling

Starchenko, V., & Миронович, С. А. (2023) · Proceedings of the Institute for System Programming of RAS · doi:10.15514/ispras-2022-35(5)-14 · Cited by 1

Dissecting Dissonance: Benchmarking Large Multimodal Models Against Self-Contradictory Instructions

Gao, J., Gan, L., Li, Y., Ye, Y., & Wang, D. Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72998-0_23 · Cited by 3

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

(2023) · doi:10.18653/v1/2023.emnlp-tutorial · Cited by 1

Dynamic Knowledge Graph Asset PricingPreprint

Li, V. X., & Tan, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4841921 · Cited by 1

Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language ModelsPreprint

Saqur, R., Kratsios, A., Horvath, B. N., Tian, J.-J., Willes, J., Krach, F., Limmer, Y., & Rudzicz, F. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4856254 · Cited by 1

Proceedings of the Fifth Workshop on Privacy in Natural Language Processing

(2024) · doi:10.18653/v1/2024.privatenlp-1 · Cited by 1

Self-Pluralising Culture Alignment for Large Language ModelsPreprint

Xu, S., Leng, Y., Yu, L., & Xiong, D. (2024) · Qeios · doi:10.32388/q7ywfv · Cited by 1

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction TuningPreprint

Zhou, H., Tang, Y., Qin, H., Yang, Y., Jin, R., Xiong, D., Han, K., & Wang, Y. (2024) · Qeios · doi:10.32388/fzgmpf · Cited by 1

Think Inside the JSON: Reinforcement Strategy for Strict LLM Schema AdherencePreprint

Agarwal, B., Joshi, I., & Rojkova, V. (2025) · Preprints.org · doi:10.20944/preprints202502.1390.v1 · Cited by 1

From Informal to Formal – Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal ProofsPreprint

Cao, J., & Lu, Y. (2025) · Qeios · doi:10.32388/mlaotg · Cited by 1

Auto-Formulating Dynamic Programming Problems with Large Language ModelsPreprint

Zhou, C., Jing-yuan, Y., Li, X., Chen, Y., He, Z., & Ge, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5350161 · Cited by 16

Evidence-Informed Guidance on Cannabidiol Use in Older Adults: Development and Evaluation of Retrieval-Augmented Large Language Models (Preprint)Preprint

Abedi, A., Chu, C. H., & Khan, S. S. (2026) · doi:10.2196/preprints.91559 · Cited by 1

Fine-Tuned and Instruction-Tuned Transformer Models for Misogyny Detection in Spanish Song LyricsPreprint

González, J. S. T., & Segura-Bédmar, I. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6153646 · Cited by 1

From AI Agents to AI Scientists in Health: Emerging Landscape, Challenges, and the Path ForwardPreprint

Chen, Q., Wang, X., Zhou, R., Kim, H., Wang, S., Zhao, W., Liu, T., Riaz, I., He, L., Zhao, Y., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6889798 · Cited by 1

RefineCoder: Iterative Improving of Large Language Models via Adaptive Critique Refinement for Code Generation

Zhou, C., Zhang, X., Song, D., Chen, X., Gu, W., Ma, H., Tian, Y., Zhang, M., & Hu, L. (2025) · arXiv · doi:10.48550/arxiv.2502.09183 · Cited by 18

SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skills

Zheng, B., Fatemi, M. Y., Jin, X., Wang, Z. Z., Gandhi, A., Song, Y., Gu, Y., Srinivasa, J., Liu, G., Neubig, G., et al. (2025) · arXiv · doi:10.48550/arxiv.2504.07079

Play Favorites: A Statistical Method to Measure Self-Bias in LLM-as-a-Judge

Spiliopoulou, E., Fogliato, R., Burnsky, H., Soliman, T., Ma, J., Horwood, G., & Ballesteros, M. (2025) · arXiv · doi:10.48550/arxiv.2508.06709 · Cited by 51

YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering

D'Souza, J., Giglou, H. B., & Münch, Q. (2025) · arXiv · doi:10.48550/arxiv.2505.14279

Can Large Reasoning Models Self-Train?

Shafayat, S., Tajwar, F., Salakhutdinov, R., Schneider, J., & Zanette, A. (2025) · arXiv · doi:10.48550/arxiv.2505.21444

Adaptation of language models via text augmentation

Prachaseree, C. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/171247

Constructing a Closed-Domain Question Answering System with Generative Language Models

Le, H., Nguyen, L.-M., Ni, J., & Okada, S. (2023) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse59128.2023.10299437 · Cited by 1

A Progression of Transformer Architectures towards CognitionPreprint

Kumar, M. (2023) · doi:10.36227/techrxiv.24548053

A Progression of Transformer Architectures towards CognitionPreprint

Kumar, M. (2023) · doi:10.36227/techrxiv.24548053.v1

Research with Subsymbolic AI

Sudmann, A., Echterhölter, A., Ramsauer, M., Retkowski, F., & Schröter, J. (2023) · AI critique · doi:10.1515/9783839467664-003

End-to-End Autoregressive Retrieval via Bootstrapping for Smart Reply Systems

Towle, B., & Zhou, K. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.510

Simple and Effective Input Reformulations for Translation

Yu, B., Lillemark, H. J., & Keutzer, K. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.638

Robot Debater: Debate-styled Text Auto-generation System Based on Large Foundation Language Models

Zhu, Y., Ling, Y., Ling, X., & Yang, J. (2023) · IEEE International Conference on Pattern Recognition and Machine Learning (PRML) · doi:10.1109/prml59573.2023.10348335

Generation of Korean Offensive Language by Leveraging Large Language Models via Prompt Design

Shin, J., Song, H., Lee, H., Gaim, F., & Park, J. C. (2023) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2023.ijcnlp-main.62 · Cited by 1

Knowledge Sources

Jiang, M., Lin, B. Y., Wang, S., Xu, Y., Yu, W., & Zhu, C. (2024) · SpringerBriefs in computer science · doi:10.1007/978-981-97-0747-8_2

Tűzvédelmi szakértői rendszer létrehozása nagy nyelvi modellek segítségével

Karsa, R. (2024) · Rendvédelem · doi:10.53793/rv.2024.2.3

Grosse Sprachmodelle

Handschuh, S. (2024) · Informationswissenschaft Theorie Methode und Praxis · doi:10.18755/iw.2024.3

Large Language Model for automobilePreprint

Ding, F. (2024) · doi:10.33774/coe-2024-cdlwg

Intelligent Digital Agents in the Era of Large Language ModelsPreprint

Faught, B., Lu, H., Marshall, T., Sikka, H., Guruprasad, P., & Gauri, B. (2024) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/f75wz · Cited by 1

Transformer-Driven Models for Language, Vision, and Multimodality

Luo, M., Gokhale, T., Varshney, N., Yang, Y., & Baral, C. R. (2024) · Synthesis lectures on computer vision · doi:10.1007/978-3-031-57816-8_2

Fine-Tuning of Small/Medium LLMs for Business QA on Structured Data

Ragab, R., & Altahhan, A. (2024) · International Conference on Advanced Natural Language Processing (AdNLP) · doi:10.5121/csit.2024.141005 · Cited by 1

Research on Dataset Generation in the Development of Large Language Models for Digital Textbooks

Lee, Y. (2023) · International Conference on Robotics, Automation and Artificial Intelligence (RAAI) · doi:10.1109/raai59955.2023.10601206

Automation of Trainable Datasets Generation for Medical-Specific Language Model: Using MIMIC-IV Discharge Notes

Lee, Y., Kim, C., & Ko, T. (2024) · Studies in health technology and informatics · doi:10.3233/shti240497

Training Large Language Models to Follow System Prompt with Self-Supervised Fine-tuning

Qiu, J., & Yang, Y. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651145 · Cited by 1

Construction and Evaluation of News Commentary Chat Corpus

Narita, F., Sato, S., Tokuhisa, R., & Inui, K. (2024) · Journal of Natural Language Processing · doi:10.5715/jnlp.31.1015

Continual Dialogue State Tracking via Reason-of-Select Distillation

Feng, Y., Liu, B., Dong, X., Lu, Z., Zhan, L.-M., Wu, X.-M., & Lam, A. Y. S. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.422 · Cited by 1

Named Entity Recognition For Construction Documents Based on Fine-Tuning of Large Language Models

Zhou, J., & Ma, Z. (2024) · Creative Construction Conference (CCC) · doi:10.3311/ccc2024-175

Context-Aware and Task-Specific Prompting with Iterative Refinement for Historical TextsPreprint

Zhang, J. (2024) · Preprints.org · doi:10.20944/preprints202410.0470.v1 · Cited by 1

ServiceCoder: A Service Computing Code Generation Model

Chen, Y., Yu, L., Zhou, X., Su, H., Liu, J., & Wang, Y. (2024) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws62655.2024.00133

Sparse Mixture of Experts Language Models Excel in Knowledge Distillation

Xu, H., Liu, H., Gong, W., Deng, X., & Wang, H. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9437-9_7

Large Language Model and Application for Railway Track Management Based on Domain Specialization

Song, Y., Tang, Y., & Liu, R. (2024) · Lecture notes in civil engineering · doi:10.1007/978-981-97-9644-1_21

How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment

Huang, H., Li, Y., Sun, H., Bai, Y., & Gao, Y. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.504 · Cited by 4

Speechworthy Instruction-tuned Language Models

Cho, H. J., Jedema, N., Ribeiro, L. F. R., Sharma, K., Szekely, P. A., Moschitti, A., Janssen, R., & May, J. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.595

Weak Reward Model Transforms Generative Models into Robust Causal Event Extraction Systems

Silva, I. L. D., Yan, H., Gui, L., & He, Y. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.816

Optimizing Instruction Synthesis: Effective Exploration of Evolutionary Space with Tree Search

Li, C., Chen, Q., Li, Z., FengTao, F., Li, Y., Chen, H., Yu, F., & Zhang, Y. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.93

KNN-Instruct: Automatic Instruction Construction with K Nearest Neighbor Deduction

Kou, J., Xu, B., Zhu, C., & Mao, Z. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.577

Eliciting and Improving the Causal Reasoning Abilities of Large Language Models with Conditional Statements

Liu, X., Yin, D., Zhang, C., Zhao, D., & Feng, Y. (2025) · Computational Linguistics · doi:10.1162/coli_a_00548 · Cited by 4

CMCOQA: A Chinese Medical Complex Open-Question Answering Benchmark

Li, Z., Zhao, S., Wang, H., Xu, H., Qin, B., & Liu, T. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821873

Multi-turn Instruction Invocation on Human-Robot Interaction by Large Language Models

Cheng, B., Huang, Y. Y., Sun, X., Hu, J., Li, B., Pu, Q., Wu, Z., & Tao, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-0780-8_15

LexSage: Multi-Task Optimization in Legal Large Language Model Applications

Han, L., Zhang, Y., Gao, W., & Li, X. (2024) · International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC) · doi:10.1109/icairc64177.2024.10900222

ViFin-Gen: Efficient Vietnamese Instruction Dataset Generation Pipeline for Finance Domain

Hải, N. N., Thu, N. T. H., & Son, C. M. (2024) · International Conference on Advanced Technologies for Communications · doi:10.1109/atc63255.2024.10908340

Research on the Reasoning Ability of General Language Model Based on Chain of Thought

康, 睿. (2025) · Artificial Intelligence and Robotics Research · doi:10.12677/airr.2025.142026

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 222

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 30

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

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

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 17