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

FacTool: Factuality Detection in Generative AI - A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios

Chern, I.-C., Chern, S., Chen, S., Yuan, W., Feng, K., Zhou, C., He, J., Neubig, G., & Liu, P. (2023) · arXiv · doi:10.48550/arxiv.2307.13528 · Cited by 29

Information extraction of UV-NIR spectral data in waste water based on Large Language Model

Liang, J., Yu, X., Hong, W., & Cai, Y. (2024) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2024.124475 · Cited by 24

SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales

Xu, T., Wu, S., Diao, S., Liu, X., Wang, X., Chen, Y., & Gao, J. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.343 · Cited by 61

Chinese fine-grained financial sentiment analysis with large language models

Lan, Y., Wu, Y., Wang, X., Feng, W., & Zhang, Y. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10603-6 · Cited by 28

Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision

Luo, X., Liu, D., Kong, H., Huai, S., Chen, H., Xiong, G., & Liu, W. (2024) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3701728 · Cited by 27

MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning

Liu, B., Chen, C., Gong, Z., Liao, C., Wang, H., Lei, Z., Liang, M., Chen, D., Shen, M., Zhou, H., et al. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671609 · Cited by 23

A Proposal for a Language Model Based Cognitive Architecture

Knowles, K., Witbrock, M., Dobbie, G., & Yogarajan, V. (2024) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v2i1.27691 · Cited by 23

Transformers in source code generation: A comprehensive survey

Ghaemi, H., Alizadehsani, Z., Shahraki, A., & Corchado, J. M. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103193 · Cited by 27

From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language Models

He, Q., Zeng, J., He, Q., Liang, J., & Xiao, Y. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.637 · Cited by 51

Large Language Models for Computer-Aided Design: A Survey

Zhang, L., Le, B., Akhtar, N., Lam, S.-K., & Ngo, D. (2026) · ACM Computing Surveys · doi:10.1145/3787499 · Cited by 43

MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization

Chen, Y., Wen, Z., Fan, G., Chen, Z., Wu, W., Liu, D., Li, Z., Liu, B., & Xiao, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.215 · Cited by 32

PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning

Zhu, X., Qi, B., Zhang, K., Long, X., Lin, Z., & Zhou, B. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-long.142 · Cited by 45

GraphWiz: An Instruction-Following Language Model for Graph Computational Problems

Chen, N., Li, Y., Tang, J., & Li, J. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3672010 · Cited by 22

Human Learning by Model Feedback: The Dynamics of Iterative Prompting with Midjourney

Don-Yehiya, S., Choshen, L., & Abend, O. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.253 · Cited by 27

PreciseDebias: An Automatic Prompt Engineering Approach for Generative AI to Mitigate Image Demographic Biases

Clemmer, C., Ding, J., & Feng, Y. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00840 · Cited by 26

SeaLLMs - Large Language Models for Southeast Asia

Nguyen, X.-P., Zhang, W., Li, X., Aljunied, M., Hu, Z., Shen, C., Chia, Y. K., Li, X., Wang, J., Tan, Q., et al. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-demos.28 · Cited by 31

MapReader: a framework for learning a visual language model for map analysis

Zhang, Y., Zhang, W., Zeng, Z., Jiang, K., Li, J., Min, W., Luo, W., Guan, Q., Lin, J., & Yu, W. (2025) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2025.2455112 · Cited by 22

CodeDPO: Aligning Code Models with Self Generated and Verified Source Code

Zhang, K., Li, G., Dong, Y., Xu, J., Zhang, J., Su, J., Liu, Y., & Jin, Z. G. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.771 · Cited by 49

AutoTrial: Prompting Language Models for Clinical Trial Design

Wang, Z., Xiao, C., & Sun, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.766 · Cited by 31

STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models

Ma, M. D., Wang, X., Kung, P.-N., Brantingham, P. J., Peng, N., & Wang, W. W. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29839 · Cited by 22

A Survey of LLM Datasets: From Autoregressive Model to AI Chatbot

Du, F., Ma, X.-J., Yang, J., Liu, Y., Luo, C.-R., Wang, X., Jiang, H.-O., & Jing, X. (2024) · Journal of Computer Science and Technology · doi:10.1007/s11390-024-3767-3 · Cited by 25

So you want your private LLM at home? A survey and benchmark of methods for efficient GPTs

Tuggener, L., Sager, P., Taoudi-Benchekroun, Y., Grewe, B. F., & Stadelmann, T. (2024) · IEEE Swiss Conference on Data Science (SDS) · doi:10.1109/sds60720.2024.00036 · Cited by 23

Better Synthetic Data by Retrieving and Transforming Existing Datasets

Gandhi, S., Gala, R., Viswanathan, V., Wu, T., & Neubig, G. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.385 · Cited by 22

Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models

Poppi, S., Poppi, T., Cocchi, F., Cornia, M., Baraldi, L., & Cucchiara, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73668-1_20 · Cited by 35

OpsEval: A Comprehensive Benchmark Suite for Evaluating Large Language Models’ Capability in IT Operations Domain

Liu, Y., Pei, C., Xu, L., Chen, B., Sun, M., Zhang, Z., Sun, Y., Zhang, S., Kun, W., Zhang, H., et al. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3728572 · Cited by 43

Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study

Wang, B., Ping, W., Xu, P., McAfee, L., Liu, Z., Shoeybi, M., Dong, Y., Kuchaiev, O., Li, B., Xiao, C., et al. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.482 · Cited by 21

Research on Large Language Model for Coal Mine Equipment Maintenance Based on Multi-Source Text

Cao, X., Xu, W., Zhao, J., Duan, Y. G., & Yang, X. (2024) · Applied Sciences · doi:10.3390/app14072946 · Cited by 21

CodecLM: Aligning Language Models with Tailored Synthetic Data

Wang, Z., Li, C., Perot, V., Le, L., Miao, J., Zhang, Z., Lee, C.-Y., & Pfister, T. (2024) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2024.findings-naacl.235 · Cited by 39

LLM-Pilot: Characterize and Optimize Performance of your LLM Inference Services

Lazuka, M., Anghel, A., & Parnell, T. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41406.2024.00022 · Cited by 31

SIGFRID: Unsupervised, Platform-Agnostic Interference Detection in IoT Automation Rules

Cimino, G., & Deufemia, V. (2025) · ACM Trans. Internet Things · doi:10.1145/3722231 · Cited by 22

Generative AI and the social functions of educational assessment

Broadfoot, P., & Rockey, J. C. (2025) · Oxford Review of Education · doi:10.1080/03054985.2025.2455549 · Cited by 22

Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents

Shi, Z., Gao, S., Yan, L., Feng, Y., Chen, X., Chen, Z., Yin, D., Verberne, S., & Ren, Z. (2025) · Proceedings of the ACM on Web Conference 2025 · doi:10.1145/3696410.3714825 · Cited by 21

MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance

Xu, J., Wei, T., Hou, B., Orzechowski, P., Yang, S., Jin, R., Paulbeck, R., Wagenaar, J., Demiris, G., & Shen, L. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737393 · Cited by 23

LLMs and IoT: A Comprehensive Survey on Large Language Models and the Internet of Things

Sarhaddi, F., Nguyen, N. T., Zuniga, A., Khan, M., Hui, P., Tarkoma, S., Flores, H., & Nurmi, P. (2026) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2026.3680064 · Cited by 24

Is ChatGPT a Financial Expert? Evaluating Language Models on Financial Natural Language Processing

Guo, Y., Xu, Z., & Yang, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.58 · Cited by 29

Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models

Zhang, Z., Zhao, D., Miao, X., Oliaro, G., Zhang, Z., Li, Q., Jiang, Y., & Jia, Z. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.1 · Cited by 28

Automatic Large Language Model Evaluation via Peer Review

Chu, Z., Ai, Q., Tu, Y., Li, H., & Liu, Y. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679677 · Cited by 44

TinyAgent: Function Calling at the Edge

Erdogan, L. E., Lee, N., Jha, S., Kim, S., Tabrizi, R., Moon, S., Hooper, C. R. C., Anumanchipalli, G. K., Keutzer, K., & Gholami, A. (2024) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2024.emnlp-demo.9 · Cited by 30

Paragraph-to-Image Generation with Information-Enriched Diffusion Model

Wu, W., Li, Z., He, Y., Shou, M. Z., Shen, C., Cheng, L., Li, Y., Gao, T., & Zhang, D. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02435-1 · Cited by 38

A Tale of Tails: Model Collapse as a Change of Scaling Laws

Dohmatob, E., Feng, Y., Yang, P., Charton, F., & Kempe, J. (2024) · arXiv · doi:10.48550/arxiv.2402.07043 · Cited by 20

Empowering Large Language Models for Textual Data Augmentation

Li, Y., Ding, K., Wang, J., & Lee, K. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.756 · Cited by 30

Enhancing knowledge graph interactions: A comprehensive Text-to-Cypher pipeline with large language models

Yang, C., Li, C., Hu, X., Yu, H., & Lu, J. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104280 · Cited by 22

Learning Interpretable Style Embeddings via Prompting LLMs

Patel, A., Rao, D., Kothary, A., McKeown, K. R., & Callison-Burch, C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.1020 · Cited by 29

Making Large Language Models Better Data Creators

Lee, D., Pujara, J., Sewak, M., White, R. W., & Jauhar, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.948 · Cited by 28

PowerPulse : Power energy chat model with LLaMA model fine‐tuned on Chinese and power sector domain knowledge

Yin, C., Du, K., Nong, Q., Zhang, H., Yang, L., Yan, B., Huang, X., Wang, X., & Zhang, X. (2023) · Expert Systems · doi:10.1111/exsy.13513 · Cited by 21

RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of Large Language Models

Khaki, S., Li, J., Ma, L., Yang, L., & Ramachandra, P. (2024) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2024.findings-naacl.108 · Cited by 20

Tc-llama 2: fine-tuning LLM for technology and commercialization applications

Yeom, J., Lee, H., Byun, H., Kim, Y., Byun, J., Choi, Y., Kim, S., & Song, K. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00963-0 · Cited by 21

Multivariate graph neural networks on enhancing syntactic and semantic for aspect-based sentiment analysis

Wang, H., Qiu, X., & Tan, X. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05802-6 · Cited by 22

chatHPC: Empowering HPC users with large language models

Yin, J., Hines, J., Herron, E., Ghosal, T., Liu, H., Prentice, S., Lama, V., & Wang, F. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06637-1 · Cited by 24

The Philosopher's Stone: Trojaning Plugins of Large Language Models

Dong, T., Xue, M., Chen, G., Holland, R., Yan, M., Li, S., Liu, Z., & Zhu, H. (2025) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2025.230164 · Cited by 20

EFL secondary students’ use of ChatGPT for writing task completion pathways

Woo, D. J., Guo, K., & Susanto, H. (2025) · The Journal of Educational Research · doi:10.1080/00220671.2025.2510382 · Cited by 20

Design and evaluation of a GenAI-based personalized educational content system tailored to personality traits and emotional responses for adaptive learning

Hu, W., & Shao, Z. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100735 · Cited by 21

From Illusion to Insight: A Taxonomic Survey of Hallucination Mitigation Techniques in LLMs

Kazlaris, I., Antoniou, E. N., Diamantaras, K., & Bratsas, C. (2025) · AI · doi:10.3390/ai6100260 · Cited by 24

JASMINE: Arabic GPT Models for Few-Shot Learning

Nagoudi, E. M. B., Abdul-Mageed, M., Elmadany, A., Inciarte, A. A., & Khondaker, M. T. I. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.1040 · Cited by 25

Towards Effective Disambiguation for Machine Translation with Large Language Models

Iyer, V., Chen, P., & Birch, A. (2023) · Conference on Machine Translation (WMT) · doi:10.18653/v1/2023.wmt-1.44 · Cited by 26

Personalization and Customization of LLM Responses

Eapen, J., & Adhithyan, V. S. (2023) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.4.1223.123512 · Cited by 23

HackMentor: Fine-Tuning Large Language Models for Cybersecurity

Zhang, J., Wen, H., Deng, L., Xin, M., Li, Z., Li, L., Zhu, H., & Sun, L. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00076 · Cited by 24

StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback

Dou, S., Liu, Y., Jia, H., Zhou, E., Xiong, L., Shan, J., Huang, C., Wang, X., Fan, X., Xi, Z., et al. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.251 · Cited by 19

ASAM: Boosting Segment Anything Model with Adversarial Tuning

Li, B. H., Xiao, H., & Tang, 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/cvpr52733.2024.00355 · Cited by 24

CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation

Ziegler, I., Köksal, A., Elliott, D., & Schütze, H. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.56 · Cited by 26

Exploring Parameter-Efficient Fine-Tuning of Large Language Model on Automated Program Repair

Li, G., Zhi, C., Chen, J., Han, J., & Deng, S. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3695066 · Cited by 29

MLLM4Rec : multimodal information enhancing LLM for sequential recommendation

Wang, Y., Shi, X., & Zhao, X. (2024) · Journal of Intelligent Information Systems · doi:10.1007/s10844-024-00915-3 · Cited by 21

Self-Rewarding Language Models

Yuan, W., Pang, R. Y., Cho, K., Li, X., Sukhbaatar, S., Xu, J., & Weston, J. (2024) · arXiv · doi:10.48550/arxiv.2401.10020 · Cited by 18

Respond to Change With Constancy: Instruction-Tuning With LLM for Non-I.I.D. Network Traffic Classification

Lin, X., Xiong, G., Gou, G., Dong, W., Yu, J., Li, Z., & Xia, W. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3574971 · Cited by 19

PockEngine: Sparse and Efficient Fine-tuning in a Pocket

Zhu, L., Hu, L., Lin, J., Chen, W.-M., Wang, W.-C., Gan, C., & Han, S. (2023) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3613424.3614307 · Cited by 22

Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning

Long, Q., Wang, W., & Pan, S. J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.402 · Cited by 25

HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science

Song, Y., Miret, S., Zhang, H., & Liu, B. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.380 · Cited by 21

Specialist or Generalist? Instruction Tuning for Specific NLP Tasks

Shi, C., Su, Y., Yang, C. H., Yang, Y., & Cai, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.947 · Cited by 24

Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata

Xu, S., Liu, S., Culhane, T., Pertseva, E., Wu, M.-H., Semnani, S. J., & Lam, M. S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.353 · Cited by 26

KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models

Yu, Z., Gao, C., Yao, W., Wang, Y., Ye, W., Wang, J., Xie, X., Zhang, Y., & Zhang, S. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.325 · Cited by 43

Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis

Lin, Y., He, P., Xu, H., Xing, Y., Yamada, M., Liu, H., & Tang, J. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.401 · Cited by 46

Prompt robust large language model for Chinese medical named entity recognition

Chen, Y., Baoli, Z., Li, S., Jin, Z., Cai, Z., Wang, Y., Qiu, D., Liu, S., & Zhao, J. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104189 · Cited by 18

GPT4All: An Ecosystem of Open Source Compressed Language Models

Anand, Y., Nussbaum, Z., Treat, A., Miller, A., Guo, R., Schmidt, B., Duderstadt, B., & Mulyar, A. (2023) · Workshop for NLP Open Source Software (NLP-OSS) · doi:10.18653/v1/2023.nlposs-1.7 · Cited by 19

Agent Lumos: Unified and Modular Training for Open-Source Language Agents

Yin, D., Brahman, F., Ravichander, A., Chandu, K., Chang, K.-W., Choi, Y., & Lin, B. Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.670 · Cited by 17

Traditional Chinese Medicine Epidemic Prevention and Treatment Question-Answering Model Based on LLMs

Zhou, Z., Yang, T., & Hu, K. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385748 · Cited by 17

Instruct Me More! Random Prompting for Visual In-Context Learning

Zhang, J., Wang, B., Li, L., Nakashima, Y., & Nagahara, H. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00258 · Cited by 20

Enhancing Large Language Models-Based Code Generation by Leveraging Genetic Improvement

Pinna, G., Ravalico, D., Rovito, L., Manzoni, L., & Lorenzo, A. D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56957-9_7 · Cited by 19

Discrete Prompt Compression With Reinforcement Learning

Jung, H., & Kim, K.-J. (2024) · IEEE Access · doi:10.1109/access.2024.3403426 · Cited by 29

Synthetically generated text for supervised text analysis

Halterman, A. (2025) · Political Analysis · doi:10.1017/pan.2024.31 · Cited by 22

RoboGPT: An LLM-Based Long-Term Decision-Making Embodied Agent for Instruction Following Tasks

Chen, Y., Cui, W., Chen, Y., Tan, M., Xinyao, Z., Liu, J., Li, H., Zhao, D., & Wang, H. (2025) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2025.3543364 · Cited by 17

HyKGE: A Hypothesis Knowledge Graph Enhanced RAG Framework for Accurate and Reliable Medical LLMs Responses

Jiang, X., Zhang, R., Xu, Y., Qiu, R., Fang, Y., Wang, Z., Tang, J., Ding, H., Chu, X., Zhao, J., et al. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.580 · Cited by 17

R-PRM: Reasoning-Driven Process Reward Modeling

She, S., Liu, J., Liu, Y., Chen, J., Huang, X., & Huang, S. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.679 · Cited by 46

trlX: A Framework for Large Scale Reinforcement Learning from Human Feedback

Havrilla, A., Zhuravinskyi, M., Phung, D., Tiwari, A. K., Tow, J., Biderman, S., Anthony, Q. G., & Castricato, L. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.530 · Cited by 30

Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis

de Curtò, J., de Zarzà, I., Roig, G., & Calafate, C. T. (2024) · Signals · doi:10.3390/signals5020010 · Cited by 16

Aligning Large Language Models to a Domain-specific Graph Database for NL2GQL

Liang, Y., Tan, K., Xie, T., Tao, W., Wang, S., Lan, Y., & Qian, W. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679713 · Cited by 29

LLM Teacher-Student Framework for Text Classification With No Manually Annotated Data: A Case Study in IPTC News Topic Classification

Kuzman, T., & Ljubešić, N. (2025) · IEEE Access · doi:10.1109/access.2025.3544814 · Cited by 17

Distraction is All You Need for Multimodal Large Language Model Jailbreaking

Yang, Z., Fan, J., Yan, A., Gao, E., Lin, X., Li, T., Mo, K., & Dong, C. (2025) · 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/cvpr52734.2025.00884 · Cited by 16

Graph-Oriented Instruction Tuning of Large Language Models for Generic Graph Mining

Tan, Y., Lv, H., Zhan, P., Wang, S., & Yang, C. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3603062 · Cited by 16

Increasing alignment of large language models with language processing in the human brain

Gao, C., Ma, Z., Chen, J., Li, P., Huang, S., & Li, J. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00863-0 · Cited by 22

Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications

Zeng, Y., Yu, W., Li, Z., Ren, T., Ma, Y., Cao, J., Chen, X., & Yu, T. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.118 · Cited by 16

Self-Prompt Tuning: Enable Autonomous Role-Playing in LLMs

Kong, A., Zhao, S., Chen, H., Li, Q., Qin, Y., Zhou, J., & Sun, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3343-5_33 · Cited by 26

Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning

Yin, F., Vig, J., Laban, P., Joty, S., Xiong, C., & Wu, C.-S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.172 · Cited by 33

Deep Research: A Systematic SurveyPreprint

Shi, Z., Chen, Y., Li, H., Sun, W., Ni, S., Lyu, Y., Fan, R.-Z., Jin, B., Weng, Y., Zhu, M., et al. (2025) · Preprints.org · doi:10.20944/preprints202511.2077.v1 · Cited by 59

SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF

Dong, Y., Wang, Z., Sreedhar, M., Wu, X., & Kuchaiev, O. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.754 · Cited by 15

AEGIS2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails

Ghosh, S., Varshney, P., Sreedhar, M. N., Padmakumar, A., Rebedea, T., Varghese, J. R., & Parisien, C. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2025.naacl-long.306 · Cited by 15

Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding

Tang, F., Liu, C., Xu, Z., Hu, M., Huang, Z., Xue, H., Chen, Z., Peng, Z., Yang, Z., Zhou, S., et al. (2025) · 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/cvpr52734.2025.02435 · Cited by 19

Review and perspectives on multimodal perception, mutual cognition, and embodied execution for human–robot collaboration in Industry 5.0

Ding, K., Mao, Q., Zhang, Y., Zhang, Y., Zheng, P., & Wang, L. (2026) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2026.103280 · Cited by 15

DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model With Self-Generated Cross-Modal Alignment

Lu, K.-H., Chen, Z., Fu, S., Yang, C.-H. H., Huang, S.-F., Yang, C.-K., Yu, C.-M., Chen, C., Chen, W.-C., Huang, C.-Y., et al. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3675792 · Cited by 15

Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation

Yang, Z., Li, P., & Liu, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.109 · Cited by 24

Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Tasks

Kung, P.-N., Yin, F., Wu, D., Chang, K.-W., & Peng, N. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.112 · Cited by 36

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