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) · Neural Information Processing Systems · doi:10.48550/arxiv.1509.01626 · Cited by 7,563 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

5.7 The WORDNET lexical database of English

Ooi, V. B. Y. (2019) · Edinburgh University Press eBooks · doi:10.1515/9781474471459-050 · Cited by 2,034 · see citing works

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 · see citing works

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., & Singh, S. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.346 · Cited by 1,279 · see citing works

Neural Network Acceptability Judgments

Warstadt, A., Singh, A., & Bowman, S. R. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00290 · Cited by 984 · see citing works

TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification

Barbieri, F., Camacho-Collados, J., Anke, L. E., & Neves, L. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.148 · Cited by 756 · see citing works

It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners

Schick, T., & Schütze, H. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.185 · Cited by 747 · see citing works

Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Qin, G., & Eisner, J. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.410 · Cited by 342 · see citing works

CARER: Contextualized Affect Representations for Emotion Recognition

Saravia, E., Liu, H.-C. T., Huang, Y.-H., Wu, J., & Chen, Y.-S. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1404 · Cited by 337 · see citing works

RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Deng, M., Wang, J., Hsieh, C.-P., Wang, Y., Guo, H. W., Shu, T., Song, M., Xing, E. P., & Hu, Z. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.222 · Cited by 233 · see citing works

Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model

Luccioni, A. S., Viguier, S., & Ligozat, A.-L. (2022) · arXiv · doi:10.48550/arxiv.2211.02001 · Cited by 192 · see citing works

How many data points is a prompt worth?

Le Scao, T., & Rush, A. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.208 · Cited by 179 · see citing works

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

Bach, S., Sanh, V., Yong, Z. X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., et al. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2022.acl-demo.9 · Cited by 178 · see citing works

Large Language Models Struggle to Learn Long-Tail Knowledge

Kandpal, N., Deng, H., Roberts, A., Wallace, E., & Raffel, C. (2022) · arXiv · doi:10.48550/arxiv.2211.08411 · Cited by 143 · see citing works

Reframing Instructional Prompts to GPTk’s Language

Mishra, S. R., Khashabi, D., Baral, C. R., Choi, Y., & Hajishirzi, H. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.50 · Cited by 140 · see citing works

Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right

Holtzman, A., West, P., Shwartz, V., Choi, Y., & Zettlemoyer, L. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.564 · Cited by 127 · see citing works

Impact of Pretraining Term Frequencies on Few-Shot Reasoning

Razeghi, Y., Logan, R. L., Gardner, M., & Singh, S. (2022) · arXiv · doi:10.48550/arxiv.2202.07206 · Cited by 53 · see citing works

NorthEuraLex: a wide-coverage lexical database of Northern Eurasia

Dellert, J., Daneyko, T., Münch, A., Ladygina, A., Buch, A., Clarius, N., Grigorjew, I., Balabel, M., Boga, H. I., Baysarova, Z., et al. (2019) · Language Resources and Evaluation · doi:10.1007/s10579-019-09480-6 · Cited by 49 · see citing works

On the Relation between Sensitivity and Accuracy in In-Context Learning

Chen, Y., Zhao, C., Zhou, Y., McKeown, K. R., & He, H. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.12 · Cited by 37 · see citing works

Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts

Khashabi, D., Lyu, X., Min, S., Qin, L., Richardson, K., Welleck, S., Hajishirzi, H., Khot, T., Sabharwal, A., Singh, S., et al. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.266 · Cited by 29 · see citing works

Multi-Source Social Feedback of Online News FeedsPreprint

Moniz, N., & Torgo, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.07055 · Cited by 22 · see citing works

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 18 · see citing works

Measuring Causal Effects of Data Statistics on Language Model's `Factual' PredictionsPreprint

Elazar, Y., Kassner, N., Ravfogel, S., Feder, A., Ravichander, A., Mosbach, M., Belinkov, Y., Schütze, H., & Goldberg, Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2207.14251 · Cited by 15 · see citing works

ORCA: Interpreting Prompted Language Models via Locating Supporting Data Evidence in the Ocean of Pretraining DataPreprint

Han, X., & Tsvetkov, Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.12600 · Cited by 10 · see citing works

OPT: Open Pre-trained Transformer Language Models

Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al. (2022) · arXiv · doi:10.48550/arxiv.2205.01068

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 · see citing works

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 · see citing works

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 136 · see citing works

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 · see citing works

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 · see citing works

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 110 · see citing works

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 · see citing works

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 69 · see citing works

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

Lou, R., Zhang, K., & Yin, W. (2023) · Computational Linguistics · doi:10.1162/coli_a_00523 · Cited by 74 · see citing works

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) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.findings-emnlp.996 · Cited by 88 · see citing works

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.-C., & Yu, J.-F. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103724 · Cited by 48 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 45 · see citing works

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 42 · see citing works

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 42 · see citing works

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 · see citing works

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 29 · see citing works

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 51 · see citing works

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 23 · see citing works

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 21 · see citing works

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 18 · see citing works

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

Zhang, J., Wang, B.-W., Li, L.-Z., Nakashima, Y., & Nagahara, H. (2023) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00258 · Cited by 42 · see citing works

Prompting Considered Harmful

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

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 · see citing works

Data Curation Alone Can Stabilize In-context Learning

Chang, T.-Y., & Jia, R. (2022) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2023.acl-long.452 · Cited by 76 · see citing works

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

Li, B., Fan, S., Zhu, S., & Wen, L.-J. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113052 · Cited by 14 · see citing works

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 14 · see citing works

GENERATIVE PRE-TRAINED TRANSFORMER 3

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

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 18 · see citing works

Impact of Sample Selection on In-context Learning for Entity Extraction from Scientific Writing

Bölücü, N., Rybinski, M., & Wan, S. (2023) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.findings-emnlp.338 · Cited by 19 · see citing works

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 13 · see citing works

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

Hou, R., Wang, X., Zhang, W.-Y., Song, Z., Wang, K., Chen, Y., Liu, J.-P., & Ruan, T. (2025) · IEEE journal of biomedical and health informatics · doi:10.1109/jbhi.2025.3529682 · Cited by 16 · see citing works

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 15 · see citing works

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 17 · see citing works

“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 · see citing works

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 12 · see citing works

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 10 · see citing works

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

Liu, Q., Qin, J.-H., Ye, W.-X., Mou, H., He, Y., & Wang, K.-Z. (2024) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29832 · Cited by 10 · see citing works

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 9 · see citing works

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 8 · see citing works

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 · see citing works

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 7 · see citing works

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

Ye, X., & Durrett, G. (2023) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.41 · Cited by 14 · see citing works

Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis

Yang, S., Kim, J., Jang, J., Ye, S., Lee, H., & Seo, M. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00666 · Cited by 15 · see citing works

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 6 · see citing works

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 9 · see citing works

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

Yang, Z., Zhang, Y., Sui, D., Liu, C., Zhao, J., & Liu, K. (2023) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.331 · Cited by 45 · see citing works

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 7 · see citing works

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 10 · see citing works

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 5 · see citing works

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 5 · see citing works

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 6 · see citing works

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 · see citing works

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 · see citing works

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 8 · see citing works

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 4 · see citing works

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 3 · see citing works

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 · see citing works

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 3 · see citing works

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 · see citing works

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

Yang, C., Hong, Y., Lewis, G. A., Wu, T.-S., & Kästner, C. (2024) · International Conference on Automated Software Engineering · doi:10.1145/3691620.3695033 · Cited by 5 · see citing works

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

Zhang, J., Wang, B.-W., Liu, H., Li, L.-Z., Nakashima, Y., & Nagahara, H. (2025) · Journal of Imaging · doi:10.3390/jimaging11070232 · Cited by 5 · see citing works

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 3 · see citing works

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 · see citing works

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 3 · see citing works

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 · see citing works

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 · see citing works

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 2 · see citing works

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 2 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 7 · see citing works

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 · see citing works

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 · see citing works

ReadPrompt: A Readable Prompting Method for Reliable Knowledge Probing

Wang, Z., Ye, L., Wang, H., Kwan, W.-C., Ho, D., & Wong, K. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.501 · Cited by 1 · see citing works

Open Llama2 Models for the Lithuanian Language

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

LLM Based Physical Verification Runset Generator

Francisco, L., & Arikati, S. (2024) · ACM/IEEE Workshop on Machine Learning for CAD (MLCAD) · doi:10.1109/mlcad62225.2024.10740218 · Cited by 1 · see citing works

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

Du, Y., Zhang, Q., Fu, S., Hou, Y., & Han, H. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07214-w · Cited by 1 · see citing works

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 · see citing works

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

Tao, Y., Wang, T., Dong, Y., Liu, H., Zhang, K., Hu, X., & Li, G. (2025) · arXiv · doi:10.48550/arxiv.2510.09259 · Cited by 1 · see citing works

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

Wei, K., Ghosh, S., Menon, R. R., & Srivastava, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.471

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

Gong, Z., Liu, J., Wang, Q., Wang, J., Cai, X., Zhao, D., & Yan, R. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.995

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 42 · see citing works

Monotonic Paraphrasing Improves Generalization of Language Model Prompting

Liu, Q., Wang, F., Xu, N., Yan, T., Meng, T., & Chen, M. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.576 · Cited by 4 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works