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Collaborative Participatory Research with LLM Agents in South Asia: An Empirically-Grounded Methodological Initiative and Agenda from Field Evidence in Sri Lanka

arXiv 2411.08294 ↗First posted 13 Nov 2024cs.CYcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

The integration of artificial intelligence into development research methodologies presents unprecedented opportunities for addressing persistent challenges in participatory research, particularly in linguistically diverse regions like South Asia. Drawing from an empirical implementation in Sri Lanka's Sinhala-speaking communities, this paper presents an empirically grounded methodological framework designed to transform participatory development research, situated in the challenging multilingual context of Sri Lanka's flood-prone Nilwala River Basin. Moving beyond conventional translation and data collection tools, this framework deploys a multi-agent system architecture that redefines how data collection, analysis, and community engagement are conducted in linguistically and culturally diverse research settings. This structured agent-based approach enables participatory research that is both scalable and responsive, ensuring that community perspectives remain integral to research outcomes. Field experiences reveal the immense potential of LLM-based systems in addressing long-standing issues in development research across resource-limited regions, offering both quantitative efficiencies and qualitative improvements in inclusivity. At a broader methodological level, this research agenda advocates for AI-driven participatory research tools that maintain ethical considerations, cultural respect, and operational efficiency, highlighting strategic pathways for deploying AI systems that reinforce community agency and equitable knowledge generation, potentially informing broader research agendas across the Global South.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 19 of its 81 references that are in the map. First posted on arXiv 13 Nov 2024.

Builds on

19 of 81 references.

References · 19

  1. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  2. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  3. 2023Evaluating Large Language Models: A Comprehensive Surveycited by 200
  4. 2020Participatory Research for Low-resourced Machine Translation: A Case Study in African Languagescited by 159
  5. 2020Low-resource Languages: A Review of Past Work and Future Challengescited by 90
  6. 2024From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Futurecited by 85
  7. 2019A Survey of Code-switched Speech and Language Processingcited by 73
  8. 2022Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP Worldcited by 55
  9. 2023Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languagescited by 50
  10. 2024'Simulacrum of Stories': Examining Large Language Models as Qualitative Research Participantscited by 27
  11. 2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Modelscited by 21
  12. 2023AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systemscited by 19
7 more
  1. 2024Large Language Models in Qualitative Research: Uses, Tensions, and Intentionscited by 19
  2. 2023BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Modelscited by 16
  3. 2024Translating Across Cultures: LLMs for Intralingual Cultural Adaptationcited by 16
  4. 2024Traffic Performance GPT (TP-GPT): Real-Time Data Informed Intelligent ChatBot for Transportation Surveillance and Managementcited by 15
  5. 2024Towards Social AI: A Survey on Understanding Social Interactionscited by 15
  6. 2024Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMscited by 9
  7. 2023Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agentscited by 5

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