Human-computer interaction and visualization in natural language generation models: applications, challenges, and opportunities

Abstract

Natural language generation (NLG) models have emerged as a focal point of research within natural language processing (NLP), exhibiting remarkable performance in tasks such as text composition and dialogue generation. However, their intricate architectures and massive model parameters pose significant challenges to interpretability, limiting their applicability in high-stakes decision-making scenarios. To address this issue, human-computer interaction (HCI) and visualization techniques offer promising avenues to enhance the transparency and usability of NLG models by making their decision-making processes more interpretable. In this paper, we provide a comprehensive investigation into the roles, limitations, and impact of HCI and visualization in facilitating human understanding and control over NLG systems. We introduce a taxonomy of interaction methods and visualization techniques, categorizing three major research domains and their corresponding six key tasks in the application of NLG models. Finally, we summarize the shortcomings in the existing work and investigate the key challenges and emerging opportunities in the era of large language models (LLMs).

Graphical abstract

Keywords

human-computer interaction / visualization / natural language generation / large language models

References

[1] Petridis S, Terry M, Cai C J. PromptInfuser: how tightly coupling AI and UI design impacts designers’ workflows. In: Proceedings of 2024 ACM Designing Interactive Systems Conference. 2024, 743−756

[2] Li X, Wang Y, Wang H, Wang Y, Zhao J. NBSearch: semantic search and visual exploration of computational notebooks. In: Proceedings of 2021 CHI Conference on Human Factors in Computing Systems. 2021, 308

[3] Tang J, Sun G, Chen J, Zhang G, Chang B, Wang H, Liang R . MAVIDSQL: a model-agnostic visualization for interpretation and diagnosis of text-to-SQL tasks. IEEE Transactions on Cognitive and Developmental Systems, 2024, 16( 5): 1887–1903

[4] Peng Z, Guo Q, Tsang K W, Ma X. Exploring the effects of technological writing assistance for support providers in online mental health community. In: Proceedings of 2020 CHI Conference on Human Factors in Computing Systems. 2020, 1−15

[5] Chakrabarty T, Padmakumar V, He H. Help me write a poem: instruction tuning as a vehicle for collaborative poetry writing. In: Proceedings of 2022 Conference on Empirical Methods in Natural Language Processing. 2022, 6848−6863

[6] Petridis S, Diakopoulos N, Crowston K, Hansen M, Henderson K, Jastrzebski S, Nickerson J V, Chilton L B. AngleKindling: supporting journalistic angle ideation with large language models. In: Proceedings of 2023 CHI Conference on Human Factors in Computing Systems. 2023, 225

[7] Mao J, Huang J, Toshev A, Camburu O, Yuille A L, Murphy K. Generation and comprehension of unambiguous object descriptions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016, 11−20

[8] Hsu T Y, Huang C Y, Hsu Y C, Huang T H. Visual story post-editing. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019, 6581−6586

[9] Stefanini M, Cornia M, Baraldi L, Cascianelli S, Fiameni G, Cucchiara R . From show to tell: a survey on deep learning-based image captioning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45( 1): 539–559

[10] Padmakumar V, He H. Machine-in-the-loop rewriting for creative image captioning. In: Proceedings of 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022, 573−586

[11] Gao S, Chen X, Ren Z, Zhao D, Yan R . Meaningful answer generation of E-commerce question-answering. ACM Transactions on Information Systems (TOIS), 2021, 39( 2): 18

[12] Shin D, Hsieh G, Kim Y H. PlanFitting: tailoring personalized exercise plans with large language models. 2023, arXiv preprint arXiv: 2309.12555

[13] Wang C, Jain A, Chen D, Gu J. VizSeq: a visual analysis toolkit for text generation tasks. In: Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing: System Demonstrations. 2019, 253−258

[14] Singh N, Bernal G, Savchenko D, Glassman E L . Where to hide a stolen elephant: leaps in creative writing with multimodal machine intelligence. ACM Transactions on Computer-Human Interaction, 2023, 30( 5): 68

[15] Luo S, Ivison H, Han S C, Poon J . Local interpretations for explainable natural language processing: a survey. ACM Computing Surveys, 2024, 56( 9): 232

[16] Wu S, Yang Y, Wu W, Li R, Zhang Y, Wang G, Tan H, Liu Z, Shi L . Visual analysis of LLM-based entity resolution from scientific papers. Visual Informatics, 2025, 9( 2): 100236

[17] Li L, Zhang Y, Chen L . Personalized prompt learning for explainable recommendation. ACM Transactions on Information Systems, 2023, 41( 4): 103

[18] Nguyen A T, Kharosekar A, Krishnan S, Krishnan S, Tate E, Wallace B C, Lease M. Believe it or not: designing a human-AI partnership for mixed-initiative fact-checking. In: Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology. 2018, 189−199

[19] Zhou W, Jiang Y E, Cui P, Wang T, Xiao Z, Hou Y, Cotterell R, Sachan M. RecurrentGPT: interactive generation of (arbitrarily) long text. 2023, arXiv preprint arXiv: 2305.13304

[20] Bansal G, Wu T, Zhou J, Fok R, Nushi B, Kamar E, Ribeiro M T, Weld D. Does the whole exceed its parts? The effect of AI explanations on complementary team performance. In: Proceedings of 2021 CHI Conference on Human Factors in Computing Systems. 2021, 81

[21] Gehrmann S, Strobelt H, Rush A. GLTR: statistical detection and visualization of generated text. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2019, 111−116

[22] Hsu C Y, Chu Y W, Huang T H, Ku L W. Plot and rework: modeling storylines for visual storytelling. In: Proceedings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 2021, 4443−4453

[23] Rogers A, Kovaleva O, Rumshisky A . A primer in BERTology: what we know about how BERT works. Transactions of the Association for Computational Linguistics, 2020, 8: 842–866

[24] Zhao H, Chen H, Yang F, Liu N, Deng H, Cai H, Wang S, Yin D, Du M . Explainability for large language models: a survey. ACM Transactions on Intelligent Systems and Technology, 2024, 15( 2): 20

[25] Li J, Tang T, Zhao W X, Nie J Y, Wen J R . Pre-trained language models for text generation: a survey. ACM Computing Surveys, 2024, 56( 9): 230

[26] Voigt H, Alaçam Ö, Meuschke M, Lawonn K, Zarrieß S. The why and the how: a survey on natural language interaction in visualization. In: Proceedings of 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022, 348−374

[27] Xu P, Zhu X, Clifton D A . Multimodal learning with transformers: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45( 10): 12113–12132

[28] Celikyilmaz A, Clark E, Gao J. Evaluation of text generation: a survey. 2020, arXiv preprint arXiv: 2006.14799

[29] Wang Z J, Choi D, Xu S, Yang D. Putting humans in the natural language processing loop: a survey. In: Proceedings of the 1st Workshop on Bridging Human–Computer Interaction and Natural Language Processing. 2021, 47−52

[30] Dudley J J, Kristensson P O . A review of user interface design for interactive machine learning. ACM Transactions on Interactive Intelligent Systems (TiiS), 2018, 8( 2): 8

[31] Zamfirescu-Pereira J, Wong R Y, Hartmann B, Yang Q. Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts. In: Proceedings of 2023 CHI Conference on Human Factors in Computing Systems. 2023, 437

[32] Brath R, Keim D, Knittel J, Pan S, Sommerauer P, Strobelt H. The role of interactive visualization in explaining (large) NLP models: from data to inference. 2023, arXiv preprint arXiv: 2301.04528

[33] Yang W, Liu M, Wang Z, Liu S . Foundation models meet visualizations: challenges and opportunities. Computational Visual Media, 2024, 10( 3): 399–424

[34] Braşoveanu A M P, Andonie R. Visualizing transformers for NLP: a brief survey. In: Proceedings of the 24th International Conference Information Visualisation. 2020, 270−279

[35] Hohman F, Kahng M, Pienta R, Chau D H . Visual analytics in deep learning: an interrogative survey for the next frontiers. IEEE Transactions on Visualization and Computer Graphics, 2019, 25( 8): 2674–2693

[36] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser Ł, Polosukhin I. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 6000−6010

[37] Devlin J, Chang M W, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2019, 4171−4186

[38] Brown T B, Mann B, Ryder N, Subbiah M, Kaplan J, , . Language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. 2020, 159

[39] Touvron H, Martin L, Stone K, Albert P, Almahairi A, , . Llama 2: open foundation and fine-tuned chat models. 2023, arXiv preprint arXiv: 2307.09288

[40] Lee M, Gero K I, Chung J J Y, Shum S B, Raheja V, , . A design space for intelligent and interactive writing assistants. In: Proceedings of 2024 CHI Conference on Human Factors in Computing Systems. 2024, 1054

[41] Strobelt H, Kinley J, Krueger R, Beyer J, Pfister H, Rush A M . GenNI: human-AI collaboration for data-backed text generation. IEEE Transactions on Visualization and Computer Graphics, 2022, 28( 1): 1106–1116

[42] Berger M. Visually analyzing contextualized embeddings. In: Proceedings of 2020 IEEE Visualization Conference. 2020, 276−280

[43] Liu S, Li Z, Li T, Srikumar V, Pascucci V, Bremer P T . NLIZE: a perturbation-driven visual interrogation tool for analyzing and interpreting natural language inference models. IEEE Transactions on Visualization and Computer Graphics, 2019, 25( 1): 651–660

[44] Wang Z J, Turko R, Chau D H. Dodrio: exploring transformer models with interactive visualization. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations. 2021, 132−141

[45] Hoover B, Strobelt H, Gehrmann S. exBERT: a visual analysis tool to explore learned representations in transformer models. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2020, 187−196

[46] Hoque M N, Ghai B, Kraus K, Elmqvist N. Portrayal: leveraging NLP and visualization for analyzing fictional characters. In: Proceedings of 2023 ACM Designing Interactive Systems Conference. 2023, 74−94

[47] Coscia A, Endert A . KnowledgeVIS: interpreting language models by comparing fill-in-the-blank prompts. IEEE Transactions on Visualization and Computer Graphics, 2024, 30( 9): 6520–6532

[48] Vig J, Kryściński W, Goel K, Rajani N. SummVis: interactive visual analysis of models, data, and evaluation for text summarization. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations. 2021, 150−158

[49] Rashkin H, Celikyilmaz A, Choi Y, Gao J. PlotMachines: outline-conditioned generation with dynamic plot state tracking. In: Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing. 2020, 4274−4295

[50] Mostafazadeh N, Chambers N, He X, Parikh D, Batra D, Vanderwende L, Kohli P, Allen J. A corpus and cloze evaluation for deeper understanding of commonsense stories. In: Proceedings of 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016, 839−849

[51] Han H L, Yu J, Bournet R, Ciorascu A, Mackay W E, Beaudouin-Lafon M. Passages: interacting with text across documents. In: Proceedings of 2022 CHI Conference on Human Factors in Computing Systems. 2022, 338

[52] Zhang Z, Xu Y, Wang Y, Yao B, Ritchie D, Wu T, Yu M, Wang D, Li T J J. StoryBuddy: a human-AI collaborative Chatbot for parent-child interactive storytelling with flexible parental involvement. In: Proceedings of 2022 CHI Conference on Human Factors in Computing Systems. 2022, 218

[53] Calderwood A, Qiu V, Gero K I, Chilton L B. How novelists use generative language models: an exploratory user study. In: Proceedings of IUI ’20 Workshops. 2020

[54] Biermann O C, Ma N F, Yoon D. From tool to companion: storywriters want AI writers to respect their personal values and writing strategies. In: Proceedings of 2022 ACM Designing Interactive Systems Conference. 2022, 1209−1227

[55] Choi M, Park C, Yang S, Kim Y, Choo J, Hong S R. AILA: attentive interactive labeling assistant for document classification through attention-based deep neural networks. In: Proceedings of 2019 CHI Conference on Human Factors in Computing Systems. 2019, 230

[56] Atanasova P, Simonsen J G, Lioma C, Augenstein I. A diagnostic study of explainability techniques for text classification. In: Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing. 2020, 3256−3274

[57] Li B, Lee-Urban S, Johnston G, Riedl M. Story generation with crowdsourced plot graphs. In: Proceedings of the 27th AAAI Conference on Artificial Intelligence. 2013, 598−604

[58] Ma M D, Sun J, Yang M, Huang K H, Wen N, Singh S, Han R, Peng N. EventPlus: a temporal event understanding pipeline. In: Proceedings of 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021, 56−65

[59] Wang Y, Sun G, Zhu Z, Li T, Chen L, Liang R . E²Storyline: visualizing the relationship with triplet entities and event discovery. ACM Transactions on Intelligent Systems and Technology, 2024, 15( 1): 16

[60] Liu M, Li Y, Xu W, Liu L . Automated essay feedback generation and its impact on revision. IEEE Transactions on Learning Technologies, 2017, 10( 4): 502–513

[61] Peng Z, Liu Y, Zhou H, Xu Z, Ma X . CReBot: exploring interactive question prompts for critical paper reading. International Journal of Human-Computer Studies, 2022, 167: 102898

[62] Sun L, Chan A, Chang Y S, Dow S P. ReviewFlow: intelligent scaffolding to support academic peer reviewing. In: Proceedings of the 29th International Conference on Intelligent User Interfaces. 2024, 120−137

[63] Spinner T, Kehlbeck R, Sevastjanova R, Stähle T, Keim D A, Deussen O, El-Assady M . generAItor: tree-in-the-loop text generation for language model explainability and adaptation. ACM Transactions on Interactive Intelligent Systems, 2024, 14( 2): 14

[64] Ming Y, Cao S, Zhang R, Li Z, Chen Y, Song Y, Qu H. Understanding hidden memories of recurrent neural networks. In: Proceedings of 2017 IEEE Conference on Visual Analytics Science and Technology. 2017, 13−24

[65] Strobelt H, Gehrmann S, Behrisch M, Perer A, Pfister H, Rush A M . Seq2seq-Vis: a visual debugging tool for sequence-to-sequence models. IEEE Transactions on Visualization and Computer Graphics, 2019, 25( 1): 353–363

[66] Ming Y, Xu P, Cheng F, Qu H, Ren L . ProtoSteer: steering deep sequence model with prototypes. IEEE Transactions on Visualization and Computer Graphics, 2020, 26( 1): 238–248

[67] Strobelt H, Hoover B, Satyanaryan A, Gehrmann S. LMdiff: a visual diff tool to compare language models. In: Proceedings of 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2021, 96−105

[68] Park C, Na I, Jo Y, Shin S, Yoo J, Kwon B C, Zhao J, Noh H, Lee Y, Choo J. SANVis: visual analytics for understanding self-attention networks. In: Proceedings of 2019 IEEE Visualization Conference. 2019, 146−150

[69] DeRose J F, Wang J, Berger M . Attention flows: analyzing and comparing attention mechanisms in language models. IEEE Transactions on Visualization and Computer Graphics, 2021, 27( 2): 1160–1170

[70] Gao L, Shao Z, Luo Z, Hu H, Turkay C, Chen S . TransforLearn: interactive visual tutorial for the transformer model. IEEE Transactions on Visualization and Computer Graphics, 2024, 30( 1): 891–901

[71] Syed S, Yousef T, Al-Khatib K, Jänicke S, Potthast M. Summary explorer: visualizing the state of the art in text summarization. In: Proceedings of 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2021, 185−194

[72] Kahng M, Tenney I, Pushkarna M, Liu M X, Wexler J, Reif E, Kallarackal K, Chang M, Terry M, Dixon L. LLM comparator: visual analytics for side-by-side evaluation of large language models. In: Proceedings of CHI Conference on Human Factors in Computing Systems. 2024, 216

[73] Arawjo I, Swoopes C, Vaithilingam P, Wattenberg M, Glassman E L. ChainForge: a visual toolkit for prompt engineering and LLM hypothesis testing. In: Proceedings of 2024 CHI Conference on Human Factors in Computing Systems. 2024, 304

[74] Guan J, Yang Z, Zhang R, Hu Z, Huang M. Generating coherent narratives by learning dynamic and discrete entity states with a contrastive framework. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence. 2023, 12836−12844

[75] Vig J. A multiscale visualization of attention in the transformer model. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2019, 37−42

[76] Tenney I, Wexler J, Bastings J, Bolukbasi T, Coenen A, Gehrmann S, Jiang E, Pushkarna M, Radebaugh C, Reif E, Yuan A. The language interpretability tool: extensible, interactive visualizations and analysis for NLP models. In: Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2020, 107−118

[77] Strobelt H, Gehrmann S, Pfister H, Rush A M . LSTMVis: a tool for visual analysis of hidden state dynamics in recurrent neural networks. IEEE Transactions on Visualization and Computer Graphics, 2018, 24( 1): 667–676

[78] Cashman D, Patterson G, Mosca A, Watts N, Robinson S, Chang R . RNNbow: visualizing learning via backpropagation gradients in RNNs. IEEE Computer Graphics and Applications, 2018, 38( 6): 39–50

[79] Clark K, Khandelwal U, Levy O, Manning C D. What does BERT look at? An analysis of BERT’s attention. In: Proceedings of 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. 2019, 276−286

[80] Reif E, Yuan A, Wattenberg M, Viegas F B, Coenen A, Pearce A, Kim B. Visualizing and measuring the geometry of BERT. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019, 8592−8600

[81] Kovaleva O, Romanov A, Rogers A, Rumshisky A. Revealing the dark secrets of BERT. In: Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 2019, 4365−4374

[82] Ghazvininejad M, Karpukhin V, Gor V, Celikyilmaz A. Discourse-aware soft prompting for text generation. In: Proceedings of 2022 Conference on Empirical Methods in Natural Language Processing. 2022, 4570−4589

[83] Van Aken B, Winter B, Löser A, Gers F A. How does BERT answer questions?: a layer-wise analysis of transformer representations. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019, 1823−1832

[84] Sevastjanova R, Kalouli A, Beck C, Hauptmann H, El-Assady M . LMFingerprints: visual explanations of language model embedding spaces through layerwise contextualization scores. Computer Graphics Forum, 2022, 41( 3): 295–307

[85] Strobelt H, Webson A, Sanh V, Hoover B, Beyer J, Pfister H, Rush A M . Interactive and visual prompt engineering for ad-hoc task adaptation with large language models. IEEE Transactions on Visualization and Computer Graphics, 2022, 29( 1): 1146–1156

[86] Zhang Z, Gao J, Dhaliwal R S, Li T J J. VISAR: a human-AI argumentative writing assistant with visual programming and rapid draft prototyping. In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. 2023, 5

[87] Chung J J Y, Kim W, Yoo K M, Lee H, Adar E, Chang M. TaleBrush: sketching stories with generative pretrained language models. In: Proceedings of 2022 CHI Conference on Human Factors in Computing Systems. 2022, 209

[88] Mishra A, Danzy B, Soni U, Arunkumar A, Huang J, Kwon B C, Bryan C. PromptAid: visual prompt exploration, perturbation, testing and iteration for large language models. IEEE Transactions on Visualization and Computer Graphics, 2025, 1−14

[89] Sevastjanova R, Vogelbacher S, Spitz A, Keim D, El-Assady M. Visual comparison of text sequences generated by large language models. In: Proceedings of 2023 IEEE Visualization in Data Science. 2023, 11−20

[90] Hui J, Sprouse M L. Lettersmith: scaffolding written professional communication among college students. In: Proceedings of 2023 CHI Conference on Human Factors in Computing Systems. 2023, 703

[91] Afrin T, Kashefi O, Olshefski C, Litman D, Hwa R, Godley A. Effective interfaces for student-driven revision sessions for argumentative writing. In: Proceedings of 2021 CHI Conference on Human Factors in Computing Systems. 2021, 58

[92] Akoury N, Wang S, Whiting J, Hood S, Peng N, Iyyer M. STORIUM: a dataset and evaluation platform for machine-in-the-loop story generation. In: Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing. 2020, 6470−6484

[93] Chou J P, Siu A F, Lipka N, Rossi R, Dernoncourt F, Agrawala M. TaleStream: supporting story ideation with trope knowledge. In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. 2023, 52

[94] Sun S, Zhao W, Manjunatha V, Jain R, Morariu V, Dernoncourt F, Srinivasan B V, Iyyer M. IGA: an intent-guided authoring assistant. In: Proceedings of 2021 Conference on Empirical Methods in Natural Language Processing. 2021, 5972−5985

[95] Hoque M N, Ghai B, Elmqvist N. DramatVis personae: visual text analytics for identifying social biases in creative writing. In: Proceedings of 2022 ACM Designing Interactive Systems Conference. 2022, 1260−1276

[96] Peng Z, Wang X, Han Q, Zhu J, Ma X, Qu H. Storyfier: exploring vocabulary learning support with text generation models. In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. 2023, 46

[97] Mirowski P, Mathewson K W, Pittman J, Evans R. Co-writing screenplays and theatre scripts with language models: evaluation by industry professionals. In: Proceedings of 2023 CHI Conference on Human Factors in Computing Systems. 2023, 355

[98] Webson A, Pavlick E. Do prompt-based models really understand the meaning of their prompts? In: Proceedings of 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022, 2300−2344

[99] Cheng R, Smith-Renner A, Zhang K, Tetreault J, Jaimes-Larrarte A. Mapping the design space of human-AI interaction in text summarization. In: Proceedings of 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022, 431−455

[100] Gero K I, Long T, Chilton L B. Social dynamics of AI support in creative writing. In: Proceedings of 2023 CHI Conference on Human Factors in Computing Systems. 2023, 245

[101] Kim T S, Lee Y, Shin J, Kim Y H, Kim J. EvalLM: interactive evaluation of large language model prompts on user-defined criteria. In: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 2024, 306

[102] Sučik S, Skala D, Švec A, Hraška P, Šuppa M. Prompterator: iterate efficiently towards more effective prompts. In: Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2023, 471−478

[103] Bach S, Sanh V, Yong Z X, Webson A, Raffel C, Nayak N V, Sharma A, Kim T, Bari M S, Fevry T, Alyafeai Z, Dey M, Santilli A, Sun Z, Ben-david S, Xu C, Chhablani G, Wang H, Fries J, Al-shaibani M, Sharma S, Thakker U, Almubarak K, Tang X, Radev D, Jiang M T J, Rush A. PromptSource: an integrated development environment and repository for natural language prompts. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2022, 93−104

[104] Dang H, Goller S, Lehmann F, Buschek D. Choice over control: how users write with large language models using diegetic and non-diegetic prompting. In: Proceedings of 2023 CHI Conference on Human Factors in Computing Systems. 2023, 408

[105] Jin F, Lu J, Zhang J, Zong C . Instance-aware prompt learning for language understanding and generation. ACM Transactions on Asian and Low-Resource Language Information Processing, 2023, 22( 7): 199

[106] Wu T, Jiang E, Donsbach A, Gray J, Molina A, Terry M, Cai C J. PromptChainer: chaining large language model prompts through visual programming. In: Proceedings of 2022 CHI Conference on Human Factors in Computing Systems Extended Abstracts. 2022, 359

[107] Wu T, Terry M, Cai C J. AI chains: transparent and controllable human-AI interaction by chaining large language model prompts. In: Proceedings of 2022 CHI Conference on Human Factors in Computing Systems. 2022, 385

[108] Devare S, Koupaee M, Gunapati G, Ghosh S, Vallurupalli S, Lal Y K, Ferraro F, Chambers N, Durrett G, Mooney R, Erk K, Balasubramanian N. SAGEViz: schema generation and visualization. In: Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2023, 328−335

RIGHTS & PERMISSIONS

Higher Education Press