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

Generative Information Systems Are Great If You Can Read

Roegiest, A., & Pinkosova, Z. (2024) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3627508.3638345 · Cited by 19

Large Scale Generative AI Text Applied to Sports and Music

Baughman, A. K., Morales, E., Agarwal, R., Akay, G., Feris, R., Johnson, T., Hammer, S., & Karlinsky, L. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671542 · Cited by 16

PACE: Improving Prompt with Actor-Critic Editing for Large Language Model

Dong, Y., Luo, K., Jiang, X., Jin, Z. G., & Li, G. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.436 · Cited by 29

AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision Tasks

Yang, Z., Zeng, W., Jin, S., Qian, C., Luo, P., & Liu, W. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i21.34358 · Cited by 17

GENERATIVE PRE-TRAINED TRANSFORMER 3

Golubenko, O., & Pidmohylʹnyy, O. O. (2022) · ITSynergy · doi:10.53920/its-2022-2-2 · Cited by 15

Distilling Vision-Language Models on Millions of Videos

Zhao, Y., Zhao, L., Zhou, X., Wu, J., Chu, C.-T., Miao, H., Schroff, F., Adam, H., Liu, T., Gong, B., et al. (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/cvpr52733.2024.01245 · Cited by 23

A Middle Path for On-Premises LLM Deployment: Preserving Privacy Without Sacrificing Model Confidentiality

Huang, H., Li, Y., Jiang, B., Jiang, B., Liu, L., Liu, Z., Sun, R., & Liang, S. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.420 · Cited by 19

Counter Turing Test (CT2): AI-Generated Text Detection is Not as Easy as You May Think - Introducing AI Detectability Index (ADI)

Chakraborty, M., Tonmoy, S. M. T. I., Zaman, S. M. M., Gautam, S., Kumar, T., Sharma, K., Barman, N. R., Gupta, C., Jain, V., Chadha, A. R., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.136 · Cited by 15

Democratizing Reasoning Ability: Tailored Learning from Large Language Model

Wang, Z., Huang, S., Liu, Y., Wang, J., Song, M., Zhang, Z., Huang, H., Wei, F., Deng, W., Sun, F., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.120 · Cited by 22

Deep learning for nano-photonic materials – The solution to everything!?

Wiecha, P. R. (2023) · Current Opinion in Solid State and Materials Science · doi:10.1016/j.cossms.2023.101129 · Cited by 14

Benchmarking Large Language Models on Controllable Generation under Diversified Instructions

Chen, Y., Xu, B., Wang, Q., Liu, Y., & Mao, Z. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29734 · Cited by 27

Instruction Position Matters in Sequence Generation with Large Language Models

Liu, Y., Zeng, X., Shao, C., Meng, F., & Zhou, J. N. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.693 · Cited by 23

Evaluating Language Model Agency through NegotiationsPreprint

Davidson, T. R., Veselovsky, V., Josifoski, M., Peyrard, M., Bosselut, A., Kosiński, M., & West, R. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.04536 · Cited by 39

COSMO: A Large-Scale E-commerce Common Sense Knowledge Generation and Serving System at Amazon

Yu, C., Liu, X., Maia, J., Li, Y., Cao, T., Gao, Y., Song, Y., Goutam, R., Zhang, H., Yin, B., et al. (2024) · Companion of the 2024 International Conference on Management of Data · doi:10.1145/3626246.3653398 · Cited by 13

Large language model agent as a mechanical designer

Jadhav, Y., & Farimani, A. B. (2026) · Journal of Engineering Design · doi:10.1080/09544828.2026.2624356 · Cited by 18

LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models

Diao, S., Pan, R. N., Dong, H., Shum, K., Zhang, J., Xiong, W., & Zhang, T. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-demo.12 · Cited by 18

PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming

Gao, C., Fan, X., Sun, J., & Wang, X. (2024) · doi:10.18653/v1/2024.knowllm-1.11 · Cited by 13

Griffon: Spelling Out All Object Locations at Any Granularity with Large Language Models

Zhan, Y., Zhu, Y., Chen, Z., Yang, F., Tang, M., & Wang, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72946-1_23 · Cited by 23

Qwen-IG: A Qwen-based Instruction Generation Model for LLM Fine-tuning

Zhang, L., Liu, Y., Luo, Y., Gao, F., & Gu, J. (2024) · International Conference on Computing and Pattern Recognition (ICCPR) · doi:10.1145/3704323.3704357 · Cited by 13

Hypnos: A domain-specific large language model for anesthesiology

Wang, Z., Jiang, J., Zhan, Y., Zhou, B., Li, Y., Zhang, C., Yu, B., Ding, L., Jin, H., Peng, J., et al. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129389 · Cited by 13

Beyond Classification: Inferring Function Names in Stripped Binaries via Domain Adapted LLMs

Jiang, L., Jin, X., & Lin, Z. (2025) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2025.240797 · Cited by 16

Improving Reward Models with Synthetic Critiques

Ye, Z., Greenlee, F., Bartolo, M., Blunsom, P., Campos, J. A., & Gallé, M. (2025) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2025.findings-naacl.254 · Cited by 46

Automating Cyber Threat Intelligence and Attack Chain Generation using Cyber Security Knowledge Graphs and Large Language Models

Loevenich, J. F., Adler, E., Hürten, T., Spelter, F., Roncevic, D., & Lopes, R. R. F. (2025) · International Conference on Military Communications and Information Systems (ICMCIS) · doi:10.1109/icmcis64378.2025.11047951 · Cited by 13

Bridging finance and AI: a comprehensive survey of large language models in financial system

Khan, A. T., Li, S., & Cao, X. (2025) · Digital Finance · doi:10.1007/s42521-025-00146-3 · Cited by 16

Large Language Models for EDA: Future or Mirage?

He, Z., Pu, Y., Wu, H., Qiu, T., & Yu, B. (2025) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3736167 · Cited by 21

The development， application， and future of LLM similar to ChatGPT

Yan, H., Liu, Y., Jin, L., & Bai, X. (2023) · Journal of Image and Graphics · doi:10.11834/jig.230536 · Cited by 9

“Fifty Shades of Bias”: Normative Ratings of Gender Bias in GPT Generated English Text

Hada, R., Seth, A., Diddee, H., & Bali, K. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.115 · Cited by 12

Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning

Zhang, Y., Cai, H., Song, X., Chen, Y.-C., Sun, R., & Zheng, J. (2024) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2024.findings-naacl.22 · Cited by 18

MindLLM: Lightweight large language model pre-training, evaluation and domain application

Yang, Y., Sun, H., Li, J., Liu, R., Li, Y., Liu, Y., Gao, Y., & Huang, H. (2024) · AI Open · doi:10.1016/j.aiopen.2024.08.001 · Cited by 13

SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant

Sun, G., Qin, C., Wang, J., Chen, Z., Xu, R., & Tao, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72673-6_9 · Cited by 13

LawLLM: Intelligent Legal System with Legal Reasoning and Verifiable Retrieval

Yue, S., Liu, S., Zhou, Y., Shen, C., Wang, S., Xiao, Y., Li, B., Song, Y. S., Shen, X., Chen, W., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5569-1_19 · Cited by 13

CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models

Nie, Y., Yan, B., Guo, T., Liu, H., Wang, H., He, W., Zheng, B., Wang, W., Li, Q., Sun, W., 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.40 · Cited by 33

BAIT: Large Language Model Backdoor Scanning by Inverting Attack Target

Shen, G., Cheng, S., Zhang, Z., Tao, G., Zhang, K., Guo, H., Yan, L., Jin, X., An, S., Ma, S., et al. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00103 · Cited by 19

LLM4DEU: Fine Tuning Large Language Model for Medical Diagnosis in Outpatient and Emergency Department Visits of Neurosurgery

Wang, B., Liu, Y., Tian, H., Hua, R., Chang, K., Xia, J., Dai, X., Gao, Z., Liu, S., Wang, R., et al. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2024.9010125 · Cited by 13

Computer Audition: From Task-Specific Machine Learning to Foundation Models

Triantafyllopoulos, A., Tsangko, I., Gebhard, A., Mesaros, A., Virtanen, T. I., & Schuller, B. W. (2025) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3593952 · Cited by 16

A Survey of Large Language Models in Mental Health Disorder Detection on Social Media

Ge, Z., Hu, N., Li, D., Wang, Y., Qi, S., Xu, Y., Han, S., & Zhang, J. Y. (2025) · IEEE International Conference on Data Engineering Workshops (ICDEW) · doi:10.1109/icdew67478.2025.00027 · Cited by 15

Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Fine-tuning

Xing, J., He, Y., Zhou, M., Dong, H., Han, S., Zhang, D., & Chaudhuri, S. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.1795 · Cited by 18

Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation

Wang, S., Scells, H., Koopman, B., Potthast, M., & Zuccon, G. (2023) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3624918.3625322 · Cited by 14

Large Language Models for EDA: Future or Mirage?

He, Z., & Yu, B. (2024) · International Symposium on Physical Design · doi:10.1145/3626184.3639700 · Cited by 17

CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction Tuning

Liu, Y., Tao, S., Zhao, X., Zhu, M. L., Ma, W., Zhu, J., Su, C., Hou, Y., Zhang, M., Zhang, M., et al. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00390 · Cited by 22

Leveraging LLM-Generated Data for Detecting Depression Symptoms on Social Media

Bucur, A.-M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71736-9_14 · Cited by 14

Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models

Qian, C., Zhang, J., Yao, W., Liu, D., Yin, Z., Qiao, Y., Liu, Y., & Shao, J. (2024) · Findings of the Association for Computational Linguistics ACL 2024 · doi:10.18653/v1/2024.findings-acl.290 · Cited by 12

Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models

Sun, Y., Liu, C., Zhou, K., Huang, J., Song, R., Zhao, X., Zhang, F., Zhang, D., & Gai, K. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.525 · Cited by 21

Heterogeneous Value Alignment Evaluation for Large Language Models

Zhang, Z., Zhang, C., Liu, N., Qi, S., Rong, Z., Zhu, S. C., & Yang, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-00800-8_34 · Cited by 22

EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework

Shi, Y., Liang, R., & Xu, Y. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2025.acl-long.1576 · Cited by 11

Mono- and Multilingual GPT-3 Models for Hungarian

Yang, Z. G., Laki, L. J., Váradi, T., & Prószéky, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40498-6_9 · Cited by 15

Prompting with Pseudo-Code Instructions

Mishra, M., Kumar, P., Bhat, R. A., Murthy, R., Contractor, D., & Tamilselvam, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.939 · Cited by 18

ModelMate: A recommender for textual modeling languages based on pre-trained language models

da Costa, C. T. O. P., López, J. A. H., & Cuadrado, J. S. (2024) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS) · doi:10.1145/3640310.3674089 · Cited by 13

ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback

Wu, Q., Liu, W., Luan, J., & Wang, B. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.1018 · Cited by 24

Paraphrase Types Elicit Prompt Engineering Capabilities

Wahle, J. P., Ruas, T., Xu, Y., & Gipp, B. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.617 · Cited by 11

Empowering IoT-Based Autonomous Driving via Federated Instruction Tuning With Feature Diversity

Chen, J., He, J., Chen, F., Lv, Z., Tang, J., & Jia, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3518615 · Cited by 13

OntoTune: Ontology-Driven Self-training for Aligning Large Language Models

Liu, Z., Gan, C. G., Wang, J., Zhang, Y., Bo, Z., Sun, M., Chen, H., & Zhang, W. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714816 · Cited by 14

Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey

Winata, G. I., Zhao, H., Das, A., Tang, W., Yao, D. D. W., Zhang, S.-X., & Sahu, S. (2025) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.17541 · Cited by 30

exLong: Generating Exceptional Behavior Tests with Large Language Models

Zhang, J., Liu, Y., Nie, P., Li, J. J., & Gligoric, M. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00176 · Cited by 11

AlignFormer: Modality Matching Can Achieve Better Zero-Shot Instruction-Following Speech-LLM

Fan, R., Ren, B., Hu, Y., Zhao, R., Liu, S., & Li, J. (2025) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2025.3588378 · Cited by 11

Reviewing clinical knowledge in medical large language models: Training and beyond

Li, Q. Y., Liu, H., Guo, C., Gao, C., Chen, D., Wang, M., Gao, F., van Harmelen, F., & Gu, J. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114215 · Cited by 14

Precision Health in the Age of Large Language Models

Poon, H., Naumann, T., Zhang, S., & Hernández, J. G. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599568 · Cited by 13

Advanced natural-based interaction for the ITAlian language: LLaMAntino-3-ANITA

Polignano, M., Basile, P., & Semeraro, G. M. (2026) · Scientific Reports · doi:10.1038/s41598-025-31319-0 · Cited by 40

Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA

Heineman, D., Dou, Y., Maddela, M., & Xu, W. H. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.211 · Cited by 14

Generalizable clinical note section identification with large language models

Zhou, W., & Miller, T. A. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae075 · Cited by 11

Persuading across Diverse Domains: a Dataset and Persuasion Large Language Model

Jin, C., Ren, K., Kong, L., Wang, X., Song, R., & Chen, H. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.92 · Cited by 10

EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations

Zhang, C., Sun, Y., Wu, M., Chen, J., Lei, J., Abdul-Mageed, M., Jin, R., Liu, A., Zhu, J., Park, S., et al. (2024) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3640457.3688185 · Cited by 10

Agent-DA: Enhancing low-resource event extraction with collaborative multi-agent data augmentation

Tian, X., Guo, Y., Ge, B., Yuan, X., Zhang, H., Yang, Y., Ke, W., & Li, G. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112625 · Cited by 10

LLM See, LLM Do: Leveraging Active Inheritance to Target Non-Differentiable Objectives

Shimabucoro, L., Ruder, S., Kreutzer, J., Fadaee, M., & Hooker, S. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.521 · Cited by 24

The Nonlinear Dynamics of CO2 Emissions in Pakistan: A Comprehensive Analysis of Transportation, Electricity Consumption, and Foreign Direct Investment

Adeel, M., Wang, B., Ke, J., & Mvitu, I. M. (2024) · Sustainability · doi:10.3390/su17010189 · Cited by 10

Improving unified information extraction in Chinese mental health domain with instruction-tuned LLMs and type-verification component

Cai, Z., Fang, H., Liu, J., Xu, G., Long, Y., Guan, Y., & Ke, T. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103087 · Cited by 10

Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categories

Wang, T., Jiao, X., Zhu, Y., Chen, Z., He, Y., Chu, X., Gao, J., Wang, Y., & Ma, L. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714640 · Cited by 37

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning

Bai, Y., Du, X., Liang, Y., Jin, L., Zhou, J., Liu, Z., Fang, F., Chang, M., Zheng, T., Zhang, X., et al. (2025) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2025.findings-naacl.457 · Cited by 14

Leveraging AI ‐generated synthetic data to train natural language processing models for qualitative feedback analysis

Fuchs, S., Werth, A., Méndez, C., & Butcher, J. T. (2025) · Journal of Engineering Education · doi:10.1002/jee.70033 · Cited by 10

TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection

Ma, Z., Wang, P., Huang, M., Wang, J., Wu, K., Lv, X., Pang, Y.-J., Yang, Y., Tang, W., & Kang, Y. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755835 · Cited by 10

A Survey of Progress in LLM Alignment From the Perspective of Reward Design

Ji, M., Wu, Y., Wu, Z., Wang, S., Yang, J., Dras, M., & Naseem, U. (2026) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2026.3654921 · Cited by 24

EDDA: An Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection

Ding, D., Dong, L., Huang, Z., Xu, G., Huang, X., Liu, B., Jing, L., & Zhang, B. (2024) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/5fvfixthhpye · Cited by 10

Feature Extraction Method Using Lag Operation for Sub-Grouped Multidimensional Time Series Data

Okadome, Y., & Nakamura, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3429529 · Cited by 10

Domain-Specific Improvement on Psychotherapy Chatbot Using Assistant

Kang, C., Novák, D., Urbanova, K., Cheng, Y., & Hu, Y. S. (2024) · IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) · doi:10.1109/icasspw62465.2024.10626529 · Cited by 12

A New Pipeline for Generating Instruction Dataset via RAG and Self Fine-Tuning

Sung, C.-W., Lee, Y.-K., & Tsai, Y.-T. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00371 · Cited by 11

Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations

Achintalwar, S., Baldini, I., Bouneffouf, D., Byamugisha, J., Chang, M., Dognin, P., Farchi, E., Makondo, N., Mojsilović, A., Nagireddy, M., et al. (2024) · IEEE Internet Computing · doi:10.1109/mic.2024.3453671 · Cited by 10

AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs

Mahmud, Q. I., TehraniJamsaz, A., Phan, H., Chen, L., Capotă, M., Willke, T. L., Ahmed, N. K., & Jannesari, 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-long.593 · Cited by 10

People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection

Sen, I., Assenmacher, D., Samory, M., Augenstein, I., van der Aalst, W. M. P., & Wagner, C. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.649 · Cited by 18

Towards an End-to-End Personal Fine-Tuning Framework for AI Value Alignment

Watson, E. ‘., Viana, T., Zhang, S., Sturgeon, B., & Petersson, L. (2024) · Electronics · doi:10.3390/electronics13204044 · Cited by 12

A Generalize Hardware Debugging Approach for Large Language Models Semi-Synthetic, Datasets

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