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

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

Doris, A. C., Grandi, D., Tomich, R., Alam, M. F., Ataei, M., Cheong, H., & Ahmed, F. (2024) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4067333 · Cited by 76

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

Cheng, W., Zhang, Y., Zhao, X., Zeng, Z., Wang, Z., Lin, J., Guan, Q., & Yu, W. (2024) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2024.2438937 · Cited by 60

Unlearn What You Want to Forget: Efficient Unlearning for LLMs

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

Towards evaluating and building versatile large language models for medicine

Wu, C., Qiu, P., Liu, J., Gu, H., Li, N., Zhang, Y., Wang, Y., & Xie, W. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01390-4 · Cited by 52

Jailbreak and Guard Aligned Language Models With Only Few In-Context Demonstrations

Wei, Z., Wang, Y., Li, A., Mo, Y., & Wang, Y. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3660147 · Cited by 51

CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions

Liu, C., Sun, K., Zhou, Q., Duan, Y., Shu, J., Kan, H., Gu, Z., & Hu, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-56874-w · Cited by 52

FuzzLLM: A Novel and Universal Fuzzing Framework for Proactively Discovering Jailbreak Vulnerabilities in Large Language Models

Yao, D., Zhang, J., Harris, I. G., & Carlsson, M. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448041 · Cited by 74

SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling

Kim, S., Kim, D., Park, C., Lee, W., Song, W., Kim, Y., Kim, H., Kim, Y., Lee, H., Kim, J., et al. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-industry.3 · Cited by 62

ChemNLP: A Natural Language-Processing-Based Library for Materials Chemistry Text Data

Choudhary, K. K., & Kelley, M. L. (2023) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.3c03106 · Cited by 53

Attack Prompt Generation for Red Teaming and Defending Large Language Models

Deng, B., Wang, W., Feng, F., Deng, Y., Wang, Q., & He, X. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.143 · Cited by 106

Chain-of-Thought Reasoning in Tabular Language Models

Zheng, M., Yang, H., Jiang, W., Lin, Z., Lyu, Y., She, Q., & Wang, W. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.734 · Cited by 52

A Survey on Symbolic Knowledge Distillation of Large Language Models

Acharya, K., Velasquez, A., & Song, H. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3428519 · Cited by 56

ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning

Masry, A., Shahmohammadi, M., Parvez, M. R., Hoque, E., & Joty, S. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.619 · Cited by 81

Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models

Hu, Y., Stretcu, O., Lu, C.-T., Viswanathan, K., Hata, K., Luo, E., Krishna, R., & Fuxman, A. D. (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/cvpr52733.2024.00916 · Cited by 63

Scaling Data-Constrained Language Models

Muennighoff, N., Rush, A., Barak, B., Scao, T. L., Tazi, N., Piktus, A., Pyysalo, S., Wolf, T., & Raffel, C. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-2191 · Cited by 47

Application of question answering systems for intelligent agriculture production and sustainable management: A review

Yang, T., Mei, Y., Xu, L., Yu, H., & Chen, Y. (2024) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2024.107497 · Cited by 50

HDLdebugger: Streamlining HDL debugging with Large Language Models

Yao, X., Li, H., Chan, T., Xiao, W., Yuan, M., Huang, Y., Chen, L., & Yu, B. (2025) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3735638 · Cited by 72

GeoCode-GPT: A large language model for geospatial code generation

Hou, S., Shen, Z., Zhao, A., Liang, J., Gui, Z., Guan, X., Li, R., & Wu, H. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104456 · Cited by 53

What is wrong with your code generated by large language models? An extensive study

Dou, S., Jia, H., Wu, S., Zheng, H., Wu, M., Tao, Y., Zhang, M., Chai, M., Fan, J., Xu, N., et al. (2026) · Science China Information Sciences · doi:10.1007/s11432-025-4632-8 · Cited by 102

A Survey of Reinforcement Learning from Human Feedback

Kaufmann, T., Weng, P., Bengs, V., & Hüllermeier, E. (2023) · arXiv · doi:10.48550/arxiv.2312.14925 · Cited by 43

Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models

Yuan, C., Xie, Q., Huang, J., & Ananiadou, S. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645376 · Cited by 62

LMEye: An Interactive Perception Network for Large Language Models

Li, Y., Hu, B., Chen, X., Ma, L., Xu, Y. D., & Zhang, M. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3428317 · Cited by 58

Large language models for whole-learner support: opportunities and challenges

Mannekote, A., Davies, A., Pinto, J. D. G., Zhang, S., Olds, D., Schroeder, N. L., Lehman, B. A., Zapata‐Rivera, D., & Zhai, C. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1460364 · Cited by 45

Semantic Gesticulator: Semantics-Aware Co-Speech Gesture Synthesis

Zhang, Z., Ao, T., Zhang, Y., Gao, Q., Lin, C., Chen, B., & Liu, L. (2024) · ACM Transactions on Graphics · doi:10.1145/3658134 · Cited by 49

Instructional Fingerprinting of Large Language Models

Xu, J., Wang, F., Ma, M., Koh, P. W., Xiao, C., & Chen, M. (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.180 · Cited by 86

RecExplainer: Aligning Large Language Models for Explaining Recommendation Models

Lei, Y., Lian, J., Yao, J., Huang, X., Lian, D., & Xie, X. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671802 · Cited by 53

Opportunities for retrieval and tool augmented large language models in scientific facilities

Prince, M. H., Chan, H., Vriza, A., Zhou, T., Sastry, V. K., Luo, Y., Dearing, M. T., Harder, R., Vasudevan, R. K., & Cherukara, M. J. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01423-2 · Cited by 56

RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting

Shu, L., Luo, L., Hoskere, J., Zhu, Y., Liu, Y., Tong, S., Chen, J., & Meng, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29863 · Cited by 59

Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking

Zhuang, S., Liu, B., Koopman, B., & Zuccon, G. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.590 · Cited by 65

Rethinking Software Engineering in the Era of Foundation Models: A Curated Catalogue of Challenges in the Development of Trustworthy FMware

Hassan, A. E., Lin, D., Rajbahadur, G. K., Gallaba, K., Cogo, F. R., Chen, B., Zhang, H., Thangarajah, K., Oliva, G. A., Lin, J., et al. (2024) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3663529.3663849 · Cited by 63

DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows

Patel, A., Raffel, C., & Callison-Burch, C. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.208 · Cited by 59

Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection

Li, Z., Peng, B., He, P., & Yan, X. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.33 · Cited by 67

InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance

Wang, P., Zhang, D., Li, L., Tan, C., Wang, X., Zhang, M., Ren, K. L., Jiang, B., & Qiu, X. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.585 · Cited by 38

Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic

Bhardwaj, R., Anh, D., & Poria, S. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.762 · Cited by 78

Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction Following

Ye, S., Hwang, H., Yang, S., Yun, H., Kim, Y., & Seo, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29909 · Cited by 59

JARVIS -1: Open-World Multi-Task Agents With Memory-Augmented Multimodal Language Models

Wang, Z., Cai, S., Liu, A., Jin, Y., Hou, J., Zhang, B., Lin, H., He, Z., Zheng, Z., Yang, Y., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3511593 · Cited by 36

INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models

Chia, Y. K., Hong, P., Bing, L., & Poria, S. (2023) · arXiv · doi:10.48550/arxiv.2306.04757 · Cited by 36

MoT: Memory-of-Thought Enables ChatGPT to Self-Improve

Li, X., & Qiu, X. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.392 · Cited by 53

Generalization potential of large language models

Budnikov, M., Bykova, A., & Yamshchikov, I. P. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10827-6 · Cited by 42

Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns

Bian, N., Lin, H., Liu, P., Lu, Y., Zhang, C.-P., He, B., Han, X., & Sun, L. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3476030 · Cited by 60

Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future ProspectsPreprint

Hadi, M. U., Tashi, Q. A., Qureshi, R., Shah, A., Muneer, A., Irfan, M. I., Zafar, A., Shaikh, M. B., Akhtar, N., Hassan, S. Z., et al. (2025) · doi:10.36227/techrxiv.23589741.v8 · Cited by 49

TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction

Liu, J., Li, L., Xiang, T., Wang, B., & Qian, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.655 · Cited by 52

One-Shot Learning as Instruction Data Prospector for Large Language Models

Li, Y., Hui, B., Xia, X., Yang, J., Yang, M., Zhang, L., Si, S., Chen, L.-H., Liu, J., Liu, T., et al. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.252 · Cited by 73

In-Context Learning with Iterative Demonstration Selection

Qin, C., Zhang, A., Chen, C., Dagar, A., & Ye, W. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.438 · Cited by 72

Geospatial large language model trained with a simulated environment for generating tool-use chains autonomously

Zhang, Y., Li, J., Wang, Z., He, Z., Guan, Q., Lin, J., & Yu, W. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.104312 · Cited by 41

A foundation model for human-AI collaboration in medical literature mining

Wang, Z., Cao, L., Jin, Q., Chan, J., Wan, N., Afzali, B., Cho, H.-J., Choi, C.-I., Emamverdi, M., Gill, M. K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62058-5 · Cited by 43

PromptShield: Deployable Detection for Prompt Injection Attacks

Jacob, D., Alzahrani, H., Hu, Z., Alomair, B., & Wagner, D. (2024) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/3714393.3726501 · Cited by 64

Better Zero-Shot Reasoning with Self-Adaptive Prompting

Wan, X., Sun, R., Dai, H., Arık, S. Ö., & Pfister, T. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.216 · Cited by 56

AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Radharapu, B., Robinson, K., Aroyo, L. M., & Lahoti, P. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-industry.37 · Cited by 61

The Evolution of Large Language Model: Models, Applications and Challenges

Sindhu, B., Prathamesh, R. P., Sameera, M. B., & Kumaraswamy, S. (2024) · International Conference on Current Trends in Advanced Computing (ICCTAC) · doi:10.1109/icctac61556.2024.10581180 · Cited by 41

FOFO: A Benchmark to Evaluate LLMs’ Format-Following Capability

Xia, C., Xing, C., Du, J., Yang, X., Feng, Y., Xu, R., Yin, W., & Xiong, C. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.40 · Cited by 81

Effective Long-Context Scaling of Foundation Models

Xiong, W., Liu, J., Molybog, I., Zhang, H., Bhargava, P., Hou, R., Martin, L., Rungta, R., Sankararaman, K. A., Oguz, B., et al. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.260 · Cited by 33

Personalized Impression Generation for PET Reports Using Large Language Models

Tie, X., Shin, M., Pirasteh, A., Ibrahim, N., Huemann, Z., Castellino, S. M., Kelly, K. M., Garrett, J., Hu, J., Cho, S. Y., et al. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-00985-3 · Cited by 35

LLM vs Small Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model

Hu, L., He, H., Wang, D., Zhao, Z., Shao, Y., & Nie, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29782 · Cited by 40

POSTER: Identifying and Mitigating Vulnerabilities in LLM-Integrated Applications

Jiang, F., Xu, Z., Niu, L., Wang, B., Jia, J., Li, B., & Poovendran, R. (2024) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3634737.3659433 · Cited by 33

Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Value

Yao, J., Yi, X., Gong, Y., Wang, X., & Xie, X. (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.486 · Cited by 59

Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education

Gao, L., Lu, J., Shao, Z., Lin, Z., Yue, S., Ieong, C., Sun, Y., Zauner, R. J., Wei, Z., & Chen, S. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456145 · Cited by 44

Named entity recognition for construction documents based on fine-tuning of large language models with low-quality datasets

Zhou, J., & Ma, Z. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106151 · Cited by 35

A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook

Sun, J., Zheng, C., Xie, E., Liu, Z., Chu, R., Qiu, J., Xu, J., Ding, M., Li, H., Geng, M., et al. (2025) · ACM Computing Surveys · doi:10.1145/3729218 · Cited by 45

FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions

Weller, O., Chang, B., MacAvaney, S., Lo, K., Cohan, A., Durme, B. V., Lawrie, D., & Soldaini, L. (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.597 · Cited by 76

Aligning Large Language Models through Synthetic Feedback

Kim, S., Bae, S., Shin, J., Kang, S., Kwak, D., Yoo, K. M., & Seo, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.844 · Cited by 80

From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning

Wu, X., Yao, W., Chen, J., Pan, X., Wang, X., Liu, N., & Yu, D. (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.130 · Cited by 53

Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback

Lai, V. D., Nguyen, C., Ngo, N., Nguyen, T., Dernoncourt, F., Rossi, R. A., & Nguyen, T. H. (2023) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2023.emnlp-demo.28 · Cited by 51

Dataset Quantization

Zhou, D., Wang, K., Gu, J., Peng, X., Lian, D., Zhang, Y., You, Y., & Feng, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01578 · Cited by 35

Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Trustworthy Response Generation in Chinese

Wang, H., Zhao, S., Qiang, Z., Li, Z., Liu, C., Xi, N., Du, Y., Qin, B., & Liu, T. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3686807 · Cited by 42

Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning

Yang, Z., Pang, T., Feng, H., Wang, H., Chen, W., Zhu, M., & Liu, Q. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.58 · Cited by 32

SECURE: Benchmarking Large Language Models for Cybersecurity

Bhusal, D., Alam, T., Nguyen, L., Mahara, A., Lightcap, Z., Frazier, R., Fieblinger, R., Torales, G. L., Blakely, B., & Rastogi, N. (2024) · Annual Computer Security Applications Conference · doi:10.1109/acsac63791.2024.00019 · Cited by 50

LMM-VQA: Advancing Video Quality Assessment With Large Multimodal Models

Ge, Q., Sun, W., Zhang, Y., Li, Y., Ji, Z., Sun, F., Jui, S., Min, X., & Zhai, G. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3571788 · Cited by 44

Teaching Large Language Models to Translate with Comparison

Zeng, J., Meng, F., Yin, Y., & Zhou, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29920 · Cited by 52

Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution

Fernando, C., Banarse, D., Michalewski, H., Osindero, S., & Rocktäschel, T. (2023) · arXiv · doi:10.48550/arxiv.2309.16797 · Cited by 31

Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites

Wang, L., He, J., Li, S., Liu, N., & Lim, E. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53302-0_3 · Cited by 38

GOAT-Bench : Safety Insights to Large Multimodal Models through Meme-Based Social Abuse

Lin, H., Luo, Z., Wang, B., Yang, R., & Ma, J. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3729239 · Cited by 49

CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences

Weyssow, M., Kamanda, A., Zhou, X., & Sahraoui, H. A. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3736407 · Cited by 70

Large language model trained on clinical oncology data predicts cancer progression

Zhu, M., Lin, H., Jiang, J., Jinia, A. J., Jee, J., Pichotta, K., Waters, M., Rose, D., Schultz, N., Chalise, S., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01780-2 · Cited by 30

InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions

Wang, Y., Liu, Y., Shi, C., Li, H., Chen, C., Lu, H., & Yang, Y. (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.37 · Cited by 47

Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Chen, Z., Liu, K., Wang, Q., Zhang, W., Liu, J., Lin, D., Chen, K., & Zhao, F. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.557 · Cited by 128

To What Extent Have LLMs Reshaped the Legal Domain So Far? A Scoping Literature Review

Padiu, B., Iacob, R. C. A., Rebedea, T. E., & Dascălu, M. (2024) · Information · doi:10.3390/info15110662 · Cited by 38

LLM-as-BT-Planner: Leveraging LLMs for Behavior Tree Generation in Robot Task Planning

Ao, J., Wu, F., Wu, Y., Swiki, A., & Haddadin, S. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128454 · Cited by 30

Large Language Models Are Not Robust Multiple Choice Selectors

Zheng, C., Zhou, H., Meng, F., Zhou, J., & Huang, M. (2023) · arXiv · doi:10.48550/arxiv.2309.03882 · Cited by 29

A context-aware KG-LLM collaborated conceptual design approach for personalized products: A case in lower limbs rehabilitation assistive devices

Pan, X., Zhuang, W., Wen, S., Yu, W., Bao, J., & Li, X. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103422 · Cited by 33

Transforming Data Annotation with AI Agents: A Review of Architectures, Reasoning, Applications, and Impact

Karim, M. M., Khan, S., Van, D. H., Liu, X. L., Wang, C., & Qu, Q. (2025) · Future Internet · doi:10.3390/fi17080353 · Cited by 39

Data Diversity Matters for Robust Instruction Tuning

Bukharin, A., Li, S., Wang, Z., Yang, J., Yin, B., Li, X., Zhang, C., Zhao, T., & Jiang, H. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.195 · Cited by 60

An Interactive Agent Foundation Model

Durante, Z., Gong, R., Sarkar, B., Wake, N., Taori, R., Tang, P. C., Lakshmikanth, S. K., Schulman, K. A., Milstein, A. S., Vo, H., et al. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00350 · Cited by 28

Evaluation of Large Language Models as Solution Generators in Complex Optimization

Huang, B., Wu, X., Zhou, Y. F., Wu, J., Feng, L., Cheng, R., & Tan, K. C. (2025) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2025.3580520 · Cited by 28

BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning

Liu, R., Li, C., Ge, Y., Li, T. H., Shan, Y., & Li, G. (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/cvpr52733.2024.01296 · Cited by 54

Enhancing Robot Task Planning and Execution through Multi-Layer Large Language Models

Luan, Z., Lai, Y., Huang, R., Bai, S., Zhang, Y., Zhang, H., & Wang, Q. (2024) · Sensors · doi:10.3390/s24051687 · Cited by 31

JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration

Lin, Y., Lin, Z., Chen, H., Pan, P., Li, C., Chen, S., Wen, K., Jin, Y., Li, W., & Ding, X. (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/cvpr52734.2025.02084 · Cited by 31

Zero-shot Generative Large Language Models for Systematic Review Screening Automation

Wang, S., Scells, H., Zhuang, S., Potthast, M., Koopman, B., & Zuccon, G. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-56027-9_25 · Cited by 26

MOSS: An Open Conversational Large Language Model

Sun, T., Zhang, X., He, Z., Li, P., Cheng, Q., Liu, X., Yan, H., Shao, Y., Tang, Q., Zhang, S., et al. (2024) · Machine Intelligence Research · doi:10.1007/s11633-024-1502-8 · Cited by 48

How Can Generative Artificial Intelligence Techniques Facilitate Intelligent Research into Ancient Books?

Liu, J., Ma, X., Wang, L., & Pei, L. (2024) · Journal on Computing and Cultural Heritage · doi:10.1145/3690391 · Cited by 27

ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval

Yu, Y., Zhuang, Y., Zhang, R., Yu, M., Shen, J., & Zhang, C. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.748 · Cited by 28

Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation

Da, Y., Liu, X., Yin, F., Zhong, M., Bansal, H., Han, J., & Chang, K.-W. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.245 · Cited by 41

The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values

Kirk, H. R., Bean, A., Vidgen, B., Röttger, P., & Hale, S. A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.148 · Cited by 48

Baichuan2-Sum: Instruction Finetune Baichuan2-7B Model for Dialogue Summarization

Xiao, J., Chen, Y., Ou, Y., Yu, H., Shu, K., & Xiao, Y. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650513 · Cited by 30

LUNA: A Model-Based Universal Analysis Framework for Large Language Models

Song, D., Xie, X., Song, J., Zhu, D., Huang, Y., Juefei-Xu, F., & Ma, L. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3411928 · Cited by 33

CataLM: empowering catalyst design through large language models

Wang, L., Chen, X., Du, Y., Zhou, Y., Gao, Y., & Cui, W. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02473-0 · Cited by 25

The Framework and Implementation of Using Large Language Models to Answer Questions about Building Codes and Standards

Joffe, I., Felobes, G., Elgouhari, Y., Talebi-Kalaleh, M., Mei, Q., & Chui, Y. H. (2025) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-6037 · Cited by 27

Language models transmit behavioural traits through hidden signals in data

Cloud, A., Le, M. H., Chua, J., Betley, J., Sztyber, A., Mindermann, S., Hilton, J., Marks, S., & Evans, O. (2026) · Nature · doi:10.1038/s41586-026-10319-8 · Cited by 146

Instruct and Extract: Instruction Tuning for On-Demand Information Extraction

Jiao, Y., Zhong, M., Li, S., Zhao, R., Ouyang, S., Ji, H., & Han, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.620 · Cited by 33

PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language Models

Agiza, A., Mostagir, M., & Reda, S. (2024) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1609/aies.v7i1.31612 · Cited by 33

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