Zero-Shot Text Extraction and Topic Modeling with Prompt-Driven Large Language Models

Volume no :

6 |

Issue no :

1

Article Type :

Scholarly Article

Author :

A.Manoj Prabaharan

Published Date :

May, 2025

Publisher :

Journal of Science Technology and Research (JSTAR)

Page No: 1 - 12

Abstract : In recent years, large language models (LLMs) have demonstrated unprecedented capabilities in understanding and generating human-like text across a wide array of tasks. Among these emerging applications, zero-shot text extraction and topic modeling stand out as transformative approaches for analyzing unstructured data without the need for domain-specific training or labeled datasets. This abstract presents a conceptual and experimental overview of how prompt-driven LLMs can be utilized for zero-shot information extraction and unsupervised topic discovery, positioning them as efficient, scalable alternatives to traditional natural language processing (NLP) pipelines. Traditional information extraction and topic modeling methods—such as Named Entity Recognition (NER), relation extraction, and Latent Dirichlet Allocation (LDA)—often rely on predefined ontologies, extensive domain-specific annotations, and labor-intensive model tuning. These approaches, while effective in well-defined contexts, are limited by their rigidity, inability to generalize, and steep resource requirements. In contrast, modern LLMs like GPT-4 can infer task structures from carefully crafted prompts, enabling flexible, zero-shot performance across a diverse range of domains. This paradigm shift allows researchers and practitioners to bypass training data bottlenecks and deploy extraction and modeling capabilities rapidly, even in novel or low-resource scenarios. The methodology presented explores how LLMs can be guided through prompt engineering to extract structured information—such as entities, attributes, relationships, and events—from arbitrary text without any fine-tuning. By designing contextual prompts, users can direct the model to identify relevant concepts and convert unstructured inputs into structured outputs. For instance, a prompt might instruct the model to "List all the organizations and dates mentioned in the following news article," yielding accurate and interpretable outputs in a single step. Importantly, the performance of this zero-shot approach is enhanced by iterative prompt refinement, temperature control, and context-aware sampling strategies. In the realm of topic modeling, we demonstrate how LLMs can function as semantic clusterers by summarizing, categorizing, and labeling text corpora using zero-shot inference. Instead of relying on latent statistical distributions over words, LLMs can interpret content meaningfully, recognizing themes and generating human-readable topic descriptions. For example, a prompt such as "What are the main topics discussed across these documents?" allows the model to generate high-level themes—often with significantly higher coherence and relevance than those derived from LDA or non-negative matrix factorization (NMF). Additionally, the flexibility of prompt-driven LLMs supports dynamic topic modeling over time, multilingual corpora analysis, and domain-agnostic exploration of textual data. We evaluate the effectiveness of this approach through multiple case studies, including news analysis, scientific literature summarization, and customer feedback interpretation. Qualitative and quantitative comparisons with traditional models reveal that prompt-driven LLMs produce more contextually appropriate extractions and more intelligible topic labels, while maintaining competitive accuracy and recall. Furthermore, we address the limitations of this method, such as the potential for hallucination, prompt sensitivity, and the need for careful prompt design to avoid ambiguity or bias. These challenges highlight the importance of human-in-the-loop systems and automated prompt tuning strategies. In conclusion, the integration of zero-shot capabilities in large language models represents a significant leap forward for text extraction and topic modeling. Prompt-driven approaches enable rapid prototyping, greater adaptability, and reduced dependence on annotated corpora. As LLMs continue to evolve, their role in semantic understanding and data-driven discovery is poised to expand, opening new frontiers in automated text analysis, knowledge management, and computational social science. Future research will likely focus on improving model transparency, aligning outputs with domain-specific requirements, and developing hybrid systems that combine LLM-based inference with symbolic reasoning and structured databases.

Keyword: Zero-shot learning, text extraction, topic modeling, large language models, prompt engineering, information retrieval, unsupervised learning, natural language processing, semantic analysis, GPT-4, knowledge discovery, text mining, document clustering, contextual inference, language understanding, zero-shot inference, LLMs, unstructured data, human-in-the-loop, automated text analysis.

Reference:

1. Sidharth, S. (2017). Cybersecurity Approaches for IoT Devices in Smart City Infrastructures.
2. Sidharth, S. (2016). The Role of Artificial Intelligence in Enhancing Automated Threat Hunting 1Mr. Sidharth Sharma.
3. Srinivasan, R. (2025). Friction Stir Additive Manufacturing of AA7075/Al2O3 and Al/MgB2 Composites for Improved Wear and Radiation Resistance in Aerospace Applications. J. Environ. Nanotechnol, 14(1), 295-305.
4. Vijayalakshmi, K., Amuthakkannan, R., Ramachandran, K., & Rajkavin, S. A. (2024). Federated Learning-Based Futuristic Fault Diagnosis and Standardization in Rotating Machinery. SSRG International Journal of Electronics and Communication Engineering, 11(9), 223-236.
5. Sidharth, S. (2019). Enhancing Security of Cloud-Native Microservices with Service Mesh Technologies.
6. Sidharth, S. (2022). Zero Trust Architecture: A Key Component of Modern Cybersecurity Frameworks.
7. Sakthibalan, P., Saravanan, M., Ansal, V., Rajakannu, A., Vijayalakshmi, K., & Vani, K. D. (2023). A Federated Learning Approach for ResourceConstrained IoT Security Monitoring. In Handbook on Federated Learning (pp. 131-154). CRC Press.
8. Amuthakkannan, R., & Al Yaqoubi, M. H. A. (2023). Development of IoT based water pollution identification to avoid destruction of aquatic life and to improve the quality of water. International journal of engineering trends and technology, 71(10), 355-370.
9. Sidharth, S. (2016). Establishing Ethical and Accountability Frameworks for Responsible AI Systems.
10. Sidharth, S. (2015). AI-Driven Detection and Mitigation of Misinformation Spread in Generated Content.
11. Amuthakkannan, R., Muthuraj, M., Ademi, E., Rajesh, V., & Ahammad, S. H. (2023). Analysis of fatigue strength on friction stir lap weld AA2198/Ti6Al4V joints. Materials Today: Proceedings.
12. Prova, Nuzhat Noor Islam. “Healthcare Fraud Detection Using Machine Learning.” 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024.
13. Prova, N. N. I. (2024, August). Garbage Intelligence: Utilizing Vision Transformer for Smart Waste Sorting. In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) (pp. 1213-1219). IEEE.
14. Sidharth, S. (2023). AI-Driven Anomaly Detection for Advanced Threat Detection.
15. Sidharth, S. (2023). Homomorphic Encryption: Enabling Secure Cloud Data Processing.
16. Prova, N. N. I. (2024, August). Advanced Machine Learning Techniques for Predictive Analysis of Health Insurance. In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) (pp. 1166-1170). IEEE.
17. Prova, N. N. I. (2024, October). Improved Solar Panel Efficiency through Dust Detection Using the InceptionV3 Transfer Learning Model. In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 260-268). IEEE.
18. Devi, K., & Indoria, D. (2021). Digital Payment Service In India: A Review On Unified Payment Interface. Int. J. of Aquatic Science, 12(3), 1960-1966.
19. Devi, K., & Indoria, D. (2023). The Critical Analysis on The Impact of Artificial Intelligence on Strategic Financial Management Using Regression Analysis. Res Militaris, 13(2), 7093-7102.
20. Prova, N. N. I. (2024, October). Enhancing Fish Disease Classification in Bangladeshi Aquaculture through Transfer Learning, and LIME Interpretability Techniques. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 1157-1163). IEEE.
21. Devi, K., & Indoria, D. (2021). Role of Micro Enterprises in the Socio-Economic Development of Women–A Case Study of Koraput District, Odisha. Design Engineering, 1135-1151.
22. Indoria, D. (2021). AN APPLICATION OF FOREIGN DIRECT INVESTMENT. BIMS International Research Journal of Management and Commerce, 6(1), 01-04.
23. Sidharth, S. (2024). Strengthening Cloud Security with AI-Based Intrusion Detection Systems.
24. Sidharth, S. (2022). Enhancing Generative AI Models for Secure and Private Data Synthesis.
25. Kumar, G. H., Raja, D. K., Suresh, S., Kottamala, R., & Harsith, M. (2024, August). Vision-Guided Pick and Place Systems Using Raspberry Pi and YOLO. In 2024 2nd International Conference on Networking, Embedded and Wireless Systems (ICNEWS) (pp. 1-7). IEEE.
26. Kumar, G. H., Raja, D. K., Varun, H. D., & Nandikol, S. (2024, November). Optimizing Spatial Efficiency Through Velocity-Responsive Controller in Vehicle Platooning. In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS) (pp. 1-5). IEEE.
27. Sidharth, S. (2021). Multi-Cloud Environments: Reducing Security Risks in Distributed Architectures.
28. Sidharth, S. (2020). The Rising Threat of Deepfakes: Security and Privacy Implications.
29. Kalimuthu, S., Perumal, T., Yaakob, R., Marlisah, E., & Babangida, L. (2021, March). Human Activity Recognition based on smart home environment and their applications, challenges. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 815-819). IEEE.
30. Vidhyasagar, B. S., Lakshmanan, A. S., Abishek, M. K., & Kalimuthu, S. (2023, October). Video captioning based on sign language using yolov8 model. In IFIP International Internet of Things Conference (pp. 306-315). Cham: Springer Nature Switzerland.
31. Thamma, S. R. T. S. R. (2024). Optimization of Generative AI Costs in Multi-Agent and Multi-Cloud Systems.
32. Thamma, S. R. T. S. R. (2024). Revolutionizing Healthcare: Spatial Computing Meets Generative AI.
33. Vidhyasagar, B. S., Arvindhan, M., Arulprakash, A., Kannan, B. B., & Kalimuthu, S. (2023, November). The crucial function that clouds access security brokers play in ensuring the safety of cloud computing. In 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI) (pp. 98-102). IEEE.
34. Vidhyasagar, B. S., Harshagnan, K., Diviya, M., & Kalimuthu, S. (2023, October). Prediction of Tomato Leaf Disease Plying Transfer Learning Models. In IFIP International Internet of Things Conference (pp. 293-305). Cham: Springer Nature Switzerland.
35. Thamma, S. R. (2024). Cardiovascular image analysis: AI can analyze heart images to assess cardiovascular health and identify potential risks.
36. Thamma, S. R. T. S. R. (2024). Generative AI in Graph-Based Spatial Computing: Techniques and Use Cases.
37. Kalimuthu, S., Perumal, T., Yaakob, R., Marlisah, E., & Raghavan, S. (2024, March). Multiple human activity recognition using iot sensors and machine learning in device-free environment: Feature extraction, classification, and challenges: A comprehensive review. In AIP Conference Proceedings (Vol. 2816, No. 1). AIP Publishing.
38. Bs, V., Madamanchi, S. C., & Kalimuthu, S. (2024, February). Early Detection of Down Syndrome Through Ultrasound Imaging Using Deep Learning Strategies—A Review. In 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) (pp. 1-6). IEEE.
39. Turlapati, V. R., Vichitra, P., Raval, N., Khaja Mohinuddeen, J., & Mishra, B. R. (2024). Ethical Implications of Artificial Intelligence in Business Decision-making: A Framework for Responsible AI Adoption. Journal of Informatics Education and Research, 4(1).
40. Raju, P., Arun, R., Turlapati, V. R., Veeran, L., & Rajesh, S. (2024). Next-Generation Management on Exploring AI-Driven Decision Support in Business. In Optimizing Intelligent Systems for Cross-Industry Application (pp. 61-78). IGI Global.
41. Kalimuthu, S., Perumal, T., Marlisah, E., Yaakob, R., BS, V., & Ismail, N. H. (2024). HUMAN ACTIVITY RECOGNITION BASED ON DEVICE-FREE WI-FI SENSING: A COMPREHENSIVE REVIEW. Malaysian Journal of Computer Science, 37(3), 252-269.
42. Seshanna, M., Kumar, H., Seshanna, S., & Alur, N. (2021). THE INFLUENCE OF FINANCIAL LITERACY ON COLLECTIBLES AS AN ALTERNATIVE INVESTMENT AVENUE: EFFECTS OF FINANCIAL SKILL, FINANCIAL BEHAVIOUR AND PERCEIVED KNOWLEDGE ON INVESTORS’FINANCIAL WELLBEING. Turkish Online Journal of Qualitative Inquiry, 12(4).
43. Rao, P. S. (2008). International Business Environment. HIMALAYA PUBLISHING HOUSE 2nd Rev. ed..
44. Deshmukh, M., Ghadle, K., & Jadhav, O. (2020). An innovative approach for ranking hexagonal fuzzy numbers to solve linear programming problems. International Journal on Emerging Technologies, 11(2), 385-388.
45. Patil, R. D., & Jadhav, O. S. (2016). Some contribution of statistical techniques in big data: a review. International Journal on Recent and Innovation Trends in Computing and Communication, 4(4), 293-303.
46. Sreekanthaswamy, N., Anitha, S., Singh, A., Jayadeva, S. M., Gupta, S., Manjunath, T. C., & Selvakumar, P. (2025). Digital Tools and Methods. Enhancing School Counseling With Technology and Case Studies, 25.
47. Sreekanthaswamy, N., & Hubballi, R. B. (2024). Innovative Approaches To Fmcg Customer Journey Mapping: The Role Of Block Chain And Artificial Intelligence In Analyzing Consumer Behavior And Decision-Making. Library of Progress-Library Science, Information Technology & Computer, 44(3).
48. Kalluri, V. S. Impact of AI-Driven CRM on Customer Relationship Management and Business Growth in the Manufacturing Sector. International Journal of Innovative Science and Research Technology (IJISRT).
49. Kalluri, V. S. Optimizing Supply Chain Management in Boiler Manufacturing through AI-enhanced CRM and ERP Integration. International Journal of Innovative Science and Research Technology (IJISRT).
50. Nair, S. S., Lakshmikanthan, G., Kendyala, S. H., & Dhaduvai, V. S. (2024, October). Safeguarding Tomorrow-Fortifying Child Safety in Digital Landscape. In 2024 International Conference on Computing, Sciences and Communications (ICCSC) (pp. 1-6). IEEE.
51. Lakshmikanthan, G., Nair, S. S., Sarathy, J. P., Singh, S., Santiago, S., & Jegajothi, B. (2024, December). Mitigating IoT Botnet Attacks: Machine Learning Techniques for Securing Connected Devices. In 2024 International Conference on Emerging Research in Computational Science (ICERCS) (pp. 1-6). IEEE.
52. Kalluri, S. V. S., & Narra, S. (2024). Predictive Analytics in ADAS Development: Leveraging CRM Data for Customer-Centric Innovations in Car Manufacturing. vol, 9, 6.
53. Kalluri, V. S., Malineni, S. C., Seenivasan, M., Sakkarai, J., Kumar, D., & Ananthan, B. (2025). Enhancing manufacturing efficiency: leveraging CRM data with Lean-based DL approach for early failure detection. Bulletin of Electrical Engineering and Informatics, 14(3), 2319-2329.
54. Nair, S. S. (2023). Digital Warfare: Cybersecurity Implications of the Russia-Ukraine Conflict. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 31-40.
55. Chu, T. S., Nair, S. S., & Lakshmikanthan, G. (2022). Network Intrusion Detection Using Advanced AI Models A Comparative Study of Machine Learning and Deep Learning Approaches. International Journal of Communication Networks and Information Security (IJCNIS), 14(2), 359-365.
56. Jeyaprabha, B., & Sundar, C. (2021). The mediating effect of e-satisfaction on e-service quality and e-loyalty link in securities brokerage industry. Revista Geintec-gestao Inovacao E Tecnologias, 11(2), 931-940.
57. Jeyaprabha, B., & Sunder, C. What Influences Online Stock Traders’ Online Loyalty Intention? The Moderating Role of Website Familiarity. Journal of Tianjin University Science and Technology.
58. Lakshmikanthan, G., & Nair, S. S. (2024). Protecting Self-Driving Vehicles from attack threats. International Journal of Emerging Research in Engineering and Technology, 5(1), 16-20.
59. Sivakumar, K., Manoj Kumar, S., Saravanan, G., & Mahendran, G. (2025). Mechanical, wear, fatigue, water absorption and flammability of silane-treated Indian squid chitin powder-dispersed pineapple fiber-polyester composite. Polymer Bulletin, 82(5), 1663-1683.
60. Mahendran, G., Kumar, S. M., Uvaraja, V. C., & Anand, H. (2025). Effect of wheat husk biogenic ceramic Si3N4 addition on mechanical, wear and flammability behaviour of castor sheath fibre-reinforced epoxy composite. Journal of the Australian Ceramic Society, 1-10.
61. Jeyaprabha, B., Catherine, S., & Vijayakumar, M. (2024). Unveiling the Economic Tapestry: Statistical Insights Into India’s Thriving Travel and Tourism Sector. In Managing Tourism and Hospitality Sectors for Sustainable Global Transformation (pp. 249-259). IGI Global.
62. JEYAPRABHA, B., & SUNDAR, C. (2022). The Psychological Dimensions Of Stock Trader Satisfaction With The E-Broking Service Provider. Journal of Positive School Psychology, 3787-3795.
63. Mahendran, G., Mageswari, M., Kakaravada, I., & Rao, P. K. V. (2024). Characterization of polyester composite developed using silane-treated rubber seed cellulose toughened acrylonitrile butadiene styrene honey comb core and sunn hemp fiber. Polymer Bulletin, 81(17), 15955-15973.
64. Mahendran, G., Gift, M. M., Kakaravada, I., & Raja, V. L. (2024). Load bearing investigations on lightweight rubber seed husk cellulose–ABS 3D-printed core and sunn hemp fiber-polyester composite skin building material. Macromolecular Research, 32(10), 947-958.
65. Kumar, T. V. (2019). Cloud-Based Core Banking Systems Using Microservices Architecture.
66. Kumar, T. V. (2019). BLOCKCHAIN-INTEGRATED PAYMENT GATEWAYS FOR SECURE DIGITAL BANKING.
67. Mohanavel, V., Diwakar, G., Govindasamy, M., Singh, V., Theophilus Rajakumar, I. P., Soudagar, M. E. M., … & Alharbi, S. A. (2024). Fabrication of ramie/hemp fibers-reinforced hybrid polymer composite—A comprehensive study on biological and structural application. AIP Advances, 14(8).
68. Nadaf, A. B., Sharma, S., & Trivedi, K. K. (2024). CONTEMPORARY SOCIAL MEDIA AND IOT BASED PANDEMIC CONTROL: A ANALYTICAL APPROACH. Weser Books, 73.
69. Trivedi, K. K. (2022). A Framework of Legal Education towards Litigation-Free India. Issue 3 Indian JL & Legal Rsch., 4, 1.
70. Kumar, T. V. (2015). CLOUD-NATIVE MODEL DEPLOYMENT FOR FINANCIAL APPLICATIONS.
71. Kumar, T. V. (2018). REAL-TIME COMPLIANCE MONITORING IN BANKING OPERATIONS USING AI.
72. Trivedi, K. K. (2022). HISTORICAL AND CONCEPTUAL DEVELOPMENT OF PARLIAMENTARY PRIVILEGES IN INDIA.
73. Himanshu Gupta, H. G., & Trivedi, K. K. (2017). International water clashes and India (a study of Indian river-water treaties with Bangladesh and Pakistan).
74. Trivedi, K. K. (2017). Cultural Influences On The Effectiveness Of Women Protection Legislation.
75. Kumar, T. V. (2020). Generative AI Applications in Customizing User Experiences in Banking Apps.
76. Kumar, T. V. (2020). FEDERATED LEARNING TECHNIQUES FOR SECURE AI MODEL TRAINING IN FINTECH.
77. Hussain, M. I., Shamim, M., Ravi Sankar, A. V., Kumar, M., Samanta, K., & Sakhare, D. T. (2022). The effect of the Artificial Intelligence on learning quality & practices in higher education. Journal of Positive School Psychology, 1002-1009.
78. Prasad, V., Dangi, A. K., Tripathi, R., & Kumar, N. (2023). Educational Perspective of Intellectual Property Rights. Russian Law Journal, 11(2S), 257-268.
79. Kumar, T. V. (2022). AI-Powered Fraud Detection in Real-Time Financial Transactions.
80. Kumar, T. V. (2021). NATURAL LANGUAGE UNDERSTANDING MODELS FOR PERSONALIZED FINANCIAL SERVICES.
81. Khachariya, H. D., Naveen, S., Al-Nussairi, A. K. J., Abood, B. S. Z., Alanssari, A. I., & Shaker, Z. Y. (2024, November). Deep Learning for Workforce Planning and Analytics. In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES) (pp. 1-5). IEEE.

Zero-Shot Text Extraction

Zero-Shot Text Extraction, in this context, refers to a model’s ability to perform a task it has not been
explicitly trained on, simply by interpreting instructions embedded in the prompt. This capability
opens the door to a new generation of text extraction and topic modeling techniques that are
not constrained by the need for labeled training data or rigid schema definitions. Prompt-driven
LLMs can flexibly extract entities, relationships, events, and other structured data from raw text,
while also identifying and articulating latent themes in large document collections.
This introduction explores the convergence of prompt engineering and LLM-based inference as
an alternative to traditional NLP pipelines. Text extraction—once reliant on hand-crafted rules or
supervised models—can now be performed using general-purpose models guided by natural
language instructions. Similarly, topic modeling can shift from statistical abstractions toward
more semantically rich interpretations, where the model summarizes and labels content in ways
that are more aligned with human understanding.
Despite these advantages, the deployment of LLMs for zero-shot text processing is not without
challenges. Prompt design plays a critical role in determining model performance, and poorly
constructed prompts can lead to irrelevant or hallucinated outputs. Additionally, the models may
exhibit sensitivity to minor changes in input phrasing or context, and their black-box nature can
make interpretation and validation more difficult. These limitations call for careful
experimentation and, in many cases, the incorporation of human oversight or automated
validation layers.