Demystifying Prompts in Language Models via Perplexity Estimation

Language Models are Few-Shot Learners

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Character-level Convolutional Networks for Text ClassificationPreprint

Zhang, X., Zhao, J., & LeCun, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.01626 · Cited by 4,481

DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia

Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P. N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., et al. (2015) · Semantic Web · doi:10.3233/sw-140134 · Cited by 3,737

Learning Word Vectors for Sentiment Analysis

Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011) · Cited by 3,710

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

Prefix-Tuning: Optimizing Continuous Prompts for Generation

Li, X. L., & Liang, P. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.353 · Cited by 2,473

No Language Left Behind: Scaling Human-Centered Machine TranslationPreprint

Costa-jussà, M., Cross, J., Çelebi, O., Elbayad, M., Heafield, K., Heffernan, K., Kalbassi, E., Lam, J., Licht, D., Maillard, J., et al. (2022) · arXiv.org · doi:10.48550/arxiv.2207.04672 · Cited by 2,176

5.7 The WORDNET lexical database of English

Ooi, V. B. Y. (2019) · Edinburgh University Press eBooks · doi:10.1515/9781474471459-050 · Cited by 2,035

Making Pre-trained Language Models Better Few-shot Learners

Gao, T., Fisch, A., & Chen, D. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.295 · Cited by 1,380

A Survey on In-context Learning

Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Chang, B., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.64 · Cited by 649

Security and Privacy Challenges of Large Language Models: A Survey

Das, B. C., Amini, M. H., & Wu, Y. (2025) · ACM Computing Surveys · doi:10.1145/3712001 · Cited by 445

Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study

Gao, Y., Li, R., Croxford, E., Caskey, J., Patterson, B. W., Churpek, M. M., Miller, T. A., Dligach, D., & Afshar, M. (2024) · JMIR AI · doi:10.2196/58670 · Cited by 169

State of What Art? A Call for Multi-Prompt LLM Evaluation

Mizrahi, M., Kaplan, G., Malkin, D., Dror, R., Shahaf, D., & Stanovsky, G. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00681 · Cited by 113

Prompting meaning: a hermeneutic approach to optimising prompt engineering with ChatGPT

Henrickson, L., & Meroño-Peñuela, A. (2023) · AI & Society · doi:10.1007/s00146-023-01752-8 · Cited by 136

Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4

Chang, K. K., Cramer, M. H., Soni, S., & Bamman, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.453 · Cited by 176

Large Language Models in Cybersecurity: A Survey of Applications, Vulnerabilities, and Defense Techniques

Jaffal, N. O., Alkhanafseh, M., & Mohaisen, D. (2025) · AI · doi:10.3390/ai6090216 · Cited by 69

The Art of Creative Inquiry—From Question Asking to Prompt Engineering

Sasson-Lazovsky, G., Raz, T., & Kenett, Y. N. (2024) · The Journal of Creative Behavior · doi:10.1002/jocb.671 · Cited by 73

Large Language Model Instruction Following: A Survey of Progresses and Challenges

Lou, R., Zhang, K., & Yin, W. (2024) · Computational Linguistics · doi:10.1162/coli_a_00523 · Cited by 133

Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies

Nan, L., Zhao, Y., Zou, W., Ri, N., Tae, J., Zhang, E., Cohan, A., & Radev, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.996 · Cited by 102

An empirical study of Multimodal Entity-Based Sentiment Analysis with ChatGPT: Improving in-context learning via entity-aware contrastive learning

Yang, L., Wang, Z., Li, Z., Na, J., & Yu, J. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103724 · Cited by 46

Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models

Zhou, K., Jurafsky, D., & Hashimoto, T. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.335 · Cited by 38

A Novel Playbook for Pragmatic Trial Operations to Monitor and Evaluate Ambient Artificial Intelligence in Clinical Practice

Afshar, M., Resnik, F., Ryan, M. M., Hintzke, J., Lemmon, K. K., Sullivan, A. G., Shah, T., Stordalen, A., Oberst, M., Dambach, J., et al. (2025) · NEJM AI · doi:10.1056/aidbp2401267 · Cited by 40

Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains

Cheng, Y., Chen, J., Huang, Q. Y., Xing, Z., Xu, X., & Lu, Q. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3638247 · Cited by 35

Language models are not naysayers: an analysis of language models on negation benchmarks

Truong, T. H., Baldwin, T., Verspoor, K. M., & Cohn, T. (2023) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/2023.starsem-1.10 · Cited by 69

Language models are not naysayers: an analysis of language models on negation benchmarks

Truong, T. H., Baldwin, T., Verspoor, K., & Cohn, T. (2023) · arXiv · doi:10.48550/arxiv.2306.08189 · Cited by 34

Active Learning Principles for In-Context Learning with Large Language Models

Margatina, K., Schick, T., Αλέτρας, Ν., & Dwivedi-Yu, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.334 · Cited by 72

The language of prompting: What linguistic properties make a prompt successful?

Leidinger, A., van Rooij, R., & Shutova, E. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.618 · Cited by 68

MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning

Zhang, C., Lin, K., Yang, Z., Wang, J., Li, L., Lin, C.-C., Liu, Z., & Wang, L. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01295 · Cited by 24

Evaluation of African American Language Bias in Natural Language Generation

Deas, N., Grieser, J. A., Kleiner, S., Patton, D. U., Turcan, E., & McKeown, K. R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.421 · Cited by 41

Dr.ICL: Demonstration-Retrieved In-context Learning

Luo, M., Xu, X., Dai, Z., Pasupat, P., Kazemi, M., Baral, C. R., Imbrasaitė, V., & Zhao, V. Y. (2024) · Data Intelligence · doi:10.3724/2096-7004.di.2024.0012 · Cited by 86

Aligning generalization between humans and machines

Ilievski, F., Hammer, B., van Harmelen, F., Paaßen, B., Saralajew, S., Schmid, U., Biehl, M. L., Bolognesi, M., Dong, X., Gashteovski, K., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01109-4 · Cited by 37

Large Language Models and Medical Knowledge Grounding for Diagnosis PredictionPreprint

Gao, Y., Li, R., Croxford, E., Tesch, S., To, D., Caskey, J., Patterson, B. W., Churpek, M. M., Miller, T. A., Dligach, D., et al. (2023) · medRxiv · doi:10.1101/2023.11.24.23298641 · Cited by 24

Towards AI-Driven Healthcare: Systematic Optimization, Linguistic Analysis, and Clinicians’ Evaluation of Large Language Models for Smoking Cessation Interventions

Calle, P., Shao, R., Liu, Y., Hébert, E. T., Kendzor, D. E., Neil, J. M., Businelle, M. S., & Pan, C. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641965 · Cited by 20

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

Prompting Considered Harmful

Morris, M. R. (2024) · Communications of the ACM · doi:10.1145/3673861 · Cited by 17

PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models

Zou, W., Geng, R., Wang, B., & Jia, J. (2024) · arXiv · doi:10.48550/arxiv.2402.07867 · Cited by 16

Data Curation Alone Can Stabilize In-context Learning

Chang, T.-Y., & Jia, R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.452 · Cited by 37

CoLE: A collaborative legal expert prompting framework for large language models in law

Li, B., Fan, S., Zhu, S., & Wen, L. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113052 · Cited by 15

Pre-trained language model for code-mixed text in Indonesian, Javanese, and English using transformer

Hidayatullah, A. F., Apong, R. A. A. H. M., Lai, D. T. C., & Qazi, A. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01444-9 · Cited by 23

GENERATIVE PRE-TRAINED TRANSFORMER 3

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

Toward Human Readable Prompt Tuning: Kubrick’s The Shining is a good movie, and a good prompt too?

Shi, W., Han, X., Gonen, H., Holtzman, A., Tsvetkov, Y., & Zettlemoyer, L. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.733 · Cited by 44

impact of sample selection on in-context learning for entity extraction from scientific writing

Bölücü, N., Rybinski, M., & Wan, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.338 · Cited by 16

Can deepfakes be used to study emotion perception? A comparison of dynamic face stimuli

Becker, C., Conduit, R., Chouinard, P. A., & Laycock, R. (2024) · Behavior Research Methods · doi:10.3758/s13428-024-02443-y · Cited by 17

Decision Tree Extraction for Clinical Decision Support System With If-Else Pseudocode and PlanSelect Strategy

Hou, R., Wang, X., Zhang, W., Song, Z., Wang, K., Chen, Y., Liu, J., & Ruan, T. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3529682 · Cited by 15

A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness

Pecher, B., Srba, I., & Bieliková, M. (2024) · ACM Computing Surveys · doi:10.1145/3691339 · Cited by 19

Navigating the Risks of Using Large Language Models for Text Annotation in Social Science Research

Lin, H., & Zhang, Y. (2025) · Social Science Computer Review · doi:10.1177/08944393251366243 · Cited by 22

“AI, will you help?” How learners use Artificial Intelligence when writing

Krajka, J., & Olszak, I. (2024) · XLinguae · doi:10.18355/xl.2024.17.01.03 · Cited by 11

Exploring the Capability of LLMs in Performing Low-Level Visual Analytic Tasks on SVG Data Visualizations

Xu, Z., & Wall, E. (2024) · Visualization · doi:10.1109/vis55277.2024.00033 · Cited by 24

Performance of Large Language Models in a Computer Science Degree Program

Krüger, T., & Gref, M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-50485-3_40 · Cited by 11

Adaptive Prompt Routing for Arbitrary Text Style Transfer with Pre-trained Language Models

Liu, Q., Qin, J., Ye, W., Mou, H. L., He, Y., & Wang, K. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29832 · Cited by 11

Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations

Chen, W., Wu, C.-K., Chen, Y.-N., & Chen, H. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.968 · Cited by 33

Development of a Human Evaluation Framework and Correlation with Automated Metrics for Natural Language Generation of Medical DiagnosesPreprint

Croxford, E., Gao, Y., Patterson, B. W., To, D., Tesch, S., Dligach, D., Mayampurath, A. M., Churpek, M. M., & Afshar, M. (2024) · medRxiv · doi:10.1101/2024.03.20.24304620 · Cited by 13

Security and Privacy of Large Language Models: Threat Taxonomy, Ethical Implications, and Governance

Pribisalić, M., & Martinčić-Ipšić, S. (2026) · AI · doi:10.3390/ai7050152 · Cited by 9

Metric-Based In-context Learning: A Case Study in Text Simplification

Vadlamannati, S., & Şahin, G. G. (2023) · International Conference on Natural Language Generation · doi:10.18653/v1/2023.inlg-main.18 · Cited by 8

Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting

Ye, X., & Durrett, G. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.41 · Cited by 11

Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis

Yang, S., Kim, J., Jang, J., Ye, S., Lee, H., & Seo, M. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00666 · Cited by 12

Data Similarity is Not Enough to Explain Language Model Performance

Yauney, G., Reif, E., & Mimno, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.695 · Cited by 13

Security and privacy in LLMs: A comprehensive survey of threats and mitigation strategies

Berini, A. D. E., Jamil, N., Benrazek, A., Lakas, A., Ismail, L., Ferrag, M. A., & Lam, K. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104241 · Cited by 14

Representative Demonstration Selection for In-Context Learning with Two-Stage Determinantal Point Process

Zhao, Y., Zhang, Y., Sui, D., Cao, L., Zhao, J., & Liu, K. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.331 · Cited by 13

LLM-driven Instruction Following: Progresses and Concerns

Yin, W., Ye, Q., Liu, P., Ren, X., & Schütze, H. (2023) · doi:10.18653/v1/2023.emnlp-tutorial.4 · Cited by 8

Bridging Information-Theoretic and Geometric Compression in Language Models

Cheng, E., Kervadec, C., & Baroni, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.762 · Cited by 17

GenKP: generative knowledge prompts for enhancing large language models

Li, X., Peng, S., Yada, S., Wakamiya, S., & Aramaki, E. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06318-3 · Cited by 6

Context Example Selection for LLM Generated Relevance Assessments

McKechnie, J., McDonald, G., & Macdonald, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88708-6_19 · Cited by 6

Hint-Enhanced In-Context Learning Wakes Large Language Models Up For Knowledge-Intensive Tasks

Wang, Y., Guo, Q., Ni, X., Shi, C., Liu, L., Jiang, H., & Yang, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447527 · Cited by 9

Large Language Models for Recommender Systems: A Problem-Driven Survey

Guan, Z., Zhong, W., Liu, W., Liu, Y., Kou, H., Chi, X., & Zhao, X. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010137 · Cited by 5

Evolving Chain-of-Thought and automatic text annotation based intent classification method for wireless network

Li, B., Liu, T., Ma, J., Dai, J., & Liao, P. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111074 · Cited by 4

In-Context Demonstration Selection with Cross Entropy Difference

Iter, D., Pryzant, R., Xu, R., Wang, S., Liu, Y., Yi‐chong, X., & Zhu, C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.81 · Cited by 13

CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translation

Aswanth, M., Puduppully, R., Dabre, R., & Kunchukuttan, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.519 · Cited by 9

Broken Bags: Disrupting Service Through the Contamination of Large Language Models With Misinformation

Mo, Y., Tang, M., Lin, R., Zhou, B., & Li, X. (2025) · IEEE Access · doi:10.1109/access.2025.3582519 · Cited by 5

Language Model Guided Reinforcement Learning in Quantitative Trading

Darmanin, A., & Vella, V. (2025) · International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm67465.2025.11390898 · Cited by 3

Not All Demonstration Examples are Equally Beneficial: Reweighting Demonstration Examples for In-Context Learning

Yang, Z., Dai, D., Wang, P., & Sui, Z. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.880 · Cited by 5

EEG correlates of static and dynamic face perception: The role of naturalistic motion

Becker, C., Conduit, R., Chouinard, P. A., & Laycock, R. (2024) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2024.108986 · Cited by 3

What Is Wrong with My Model? Identifying Systematic Problems with Semantic Data Slicing

Yang, C., Hong, Y., Lewis, G. A., Wu, T., & Kästner, C. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3695033 · Cited by 5

E-InMeMo: Enhanced Prompting for Visual In-Context Learning

Zhang, J., Wang, B., Liu, H., Li, L., Nakashima, Y., & Nagahara, H. (2025) · Journal of Imaging · doi:10.3390/jimaging11070232 · Cited by 4

Engagement-Driven Content Generation with Large Language Models

Coppolillo, E., Cinus, F., Minici, M., Bonchi, F., & Manco, G. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736932 · Cited by 11

Frugal Prompting for Dialog Models

Santra, B., Basak, S., De, A., Gupta, M., & Goyal, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.290 · Cited by 3

Robustness of Named-Entity Replacements for In-Context Learning

Goodarzi, S., Kagita, N., Minn, D., Wang, S., Dessì, R., Toshniwal, S., Williams, A., Lanchantin, J., & Sinha, K. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.728 · Cited by 5

Input-oriented demonstration learning for hybrid evidence fact verification

Chen, C., Chen, W., Zheng, J., Luo, A., Cai, F., & Zhang, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123191 · Cited by 5

WITHDRAWN: Prompt Engineering GPT-4 to Answer Patient Inquiries: A Real-Time Implementation in the Electronic Health Record across Provider ClinicsPreprint

Afshar, M., Gao, Y., Wills, G. J., Wang, J., Churpek, M. M., Westenberger, C. J., Kunstman, D., Gordon, J. E., Liao, F., & Patterson, B. W. (2024) · medRxiv · doi:10.1101/2024.01.23.24301692 · Cited by 3

Systematic survey of various prompt optimization methods and their classifications

Jain, A. M., & Jindal, M. (2025) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1109/iccai66501.2025.00085 · Cited by 3

AI-Assisted Arbitrator Selection in Construction Disputes: An Expert-Calibrated Large Language Model Framework

Mobadersani, M., Candaş, A. B., Kuruoğlu, M., & Tokdemir, O. B. (2025) · Buildings · doi:10.3390/buildings16010120 · Cited by 3

Hate Speech Detection using CoT and Post-hoc Explanation through Instruction-based Fine Tuning in Large Language Models

Gupta, P., Jain, N., & Bhat, A. (2024) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic60222.2024.10575336 · Cited by 2

LLM Based Physical Verification Runset Generator

Francisco, L., & Arikati, S. (2024) · ACM/IEEE Workshop on Machine Learning for CAD (MLCAD) · doi:10.1145/3670474.3685976 · Cited by 2

Scaling Language Boundaries: A Comparative Analysis of Multilingual Question-Answering Capabilities in Large Language Models

Tiwari, A., Kadian, K., Garg, N., Kaur, S., & Dwivedi, V. K. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-75164-6_4 · Cited by 2

ContextualCoder: Adaptive In-Context Prompting for Programmatic Visual Question Answering

Shen, R., Inoue, N., Guan, D., Cai, R., Kot, A. C., & Shinoda, K. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3543043 · Cited by 1

Pragmatic inference and mapping for conversational implicature

Hu, J., Liu, Y., Ma, L., Zhang, W.-N., & Liu, T. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02741-7 · Cited by 2

ILD-Slider: A Parameter-Efficient Model for Identifying Progressive Fibrosing Interstitial Lung Disease from Chest CT Slices

Zhang, J., Wada, S., Sugimoto, K., Niitsu, T., Fukushima, K., Kida, H., Wang, B., Konishi, S., Okada, K., Nakashima, Y., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11100353 · Cited by 1

Plang: Efficient prompt engineering language for blending natural language and control flow in large language models

Hu, J., Bi, W., Zhou, Y., Zheng, J., Zhang, S., Chu, H., Wang, L., & Li, Q. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130118 · Cited by 1

TaskEval: Assessing Difficulty of Code Generation Tasks for Large Language Models

Tambon, F., Nikanjam, A., Zid, C., Khomh, F., & Antoniol, G. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3773285 · Cited by 2

TACOS: Open Tagging and Comparative Scoring for Instruction Fine-Tuning Data Selection

He, X., Yu, H., Sun, Q., Cheng, A., Zhang, T., Liu, C., & Guo, S. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209076 · Cited by 1

PRSM: A Measure to Evaluate CLIP’s Robustness Against Paraphrases

Schlegel, U., Weeber, F., Lan, J. W., & Seidl, T. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-6963-2_6 · Cited by 1

Zero-shot stance detection in practice: insights on training, prompting, and decoding with a capable lightweight LLM

Aiyappa, R., Senthilmani, S., An, J., Kwak, H., & Ahn, Y. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3540 · Cited by 14

Event-Centric Opinion Mining via In-Context Learning with ChatGPT

Wu, L., Chen, Y., Yang, J., Shi, G., Qi, X., & Deng, S. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-7224-1_7 · Cited by 1

Unnatural language processing: How do language models handle machine-generated prompts?

Kervadec, C., Franzon, F., & Baroni, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.959 · Cited by 1

ReadPrompt: A Readable Prompting Method for Reliable Knowledge Probing

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Open Llama2 Models for the Lithuanian Language

Nakvosas, A., Daniušis, P., & Mulevičius, V. (2025) · Informatica · doi:10.15388/25-infor592 · Cited by 1

LLM Based Physical Verification Runset Generator

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DCLMD: dynamic clustering and label mapping distribution for constructing in-context learning demonstrations

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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

Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models

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Leveraging Multiple Teachers for Test-Time Adaptation of Language-Guided Classifiers

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Improving Input-label Mapping with Demonstration Replay for In-context Learning

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SMOOTHIE: Label Free Language Model RoutingPreprint

Guha, N., Chen, M. F., Chow, T., Khare, I. S., & Ré, C. (2025) · Qeios · doi:10.32388/f319w4

Probability-Selected Demonstrations for Enhanced Zero-Shot in-Context Learning

Bai, B. (2024) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice63571.2024.10864087

Multi-Modal Relation Extraction Based on Shortest Dependency Path and Large Language ModelsPreprint

Xue, H., Dai, Y., Ma, J., & Guo, X. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5131704

Leveraging Correctness-Based Learning for Efficient Reasoning via Automated Process Supervision

Yao, Y., Liu, M., Deng, G., Liu, Z., Wu, D. O., Guo, Z., Wu, H., & Song, L. (2025) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2025.3581101

Is a Similar Task Useful for Few-Shot Selection? Aspect Term Extraction Using LLM

Imazato, K., & Shimada, K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97144-0_5

Influencia del formato y la complejidad de prompts en calidad de respuestas de modelos generativos

Vera, W. F. Z., & Carrillo, J. J. M. (2025) · Revista Científica de Informática ENCRIPTAR · doi:10.56124/encriptar.v8i16.011

ArMA: Mitigating Catastrophic Forgetting using Attention-Regularized Model Averaging in Continual Fine-tuning Large Language Models

Qiu, X., Cheng, L., Hao, T., & Tan, X. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3630623

The language of prompting: What linguistic properties make a prompt successful?

Leidinger, A., van Rooij, R., & Shutova, E. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.618 · Cited by 68

Monotonic Paraphrasing Improves Generalization of Language Model Prompting

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FamiCom: Further Demystifying Prompts for Language Models with Task-Agnostic Performance EstimationPreprint

Li, B.-Z., Zhou, B., Fu, X., Wang, F., Roth, D., & Chen, M. (2024) · arXiv.org · doi:10.48550/arxiv.2406.11243 · Cited by 9

How Can We Know What Language Models Know?

Jiang, Z., Xu, F. F., Araki, J., & Neubig, G. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00324 · Cited by 1,002

Do Prompt-Based Models Really Understand the Meaning of Their Prompts?

Webson, A., & Pavlick, E. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.167 · Cited by 203