Localizing Large Language Models for Inclusive International Development

Abstract Information: Our proposal introduces a transformative approach in the field of evaluation by leveraging a localized Large Language Model (LLM) to analyze over 400,000 USAID project evaluations. This method surpasses the capabilities of traditional LLMs, which are often built upon a predominantly Western understanding of development concepts and success metrics. By customizing the LLM to incorporate local perspectives and definitions of success, our model offers a nuanced analysis of the same dataset, revealing how local contexts can impact the recommendations made by LLMs drawing from the same dataset of evaluations.

The core innovation of our localized LLM is its refined ability to adjust model parameters to reflect local concepts and values, a critical advancement over traditional LLMs used in evaluation practices. These conventional models, while powerful, are not inherently designed to account for the vast diversity of cultural and contextual nuances that define success in various settings. Our approach, however, acknowledges and integrates these local perspectives directly into the evaluation analysis process. This adaptation allows the LLM to re-examine the extensive database of USAID evaluations through a lens that is more aligned with the local realities and expectations of the communities involved in or affected by development projects.

This advancement is crucial for the field of evaluation. It demonstrates that when LLMs are adapted to mirror local definitions of interventions and success, the interpretation of data can shift dramatically, offering insights that are more relevant and actionable for decision-makers. For instance, a women's empowerment intervention deemed successful using USAID's custom LLM was found to be more controversial when analyzed through the localized model. Such discrepancies highlight the importance of contextual sensitivity in the architecture of LLMS and the potential for thoughtful and ethical applications of AI to bridge this gap.

The value of this proposal to the evaluation community is twofold. Firstly, it showcases the potential of AI, specifically LLMs, to enrich the interpretation of evaluations at scale, providing a tool capable of handling complex datasets with a level of cultural and contextual understanding previously unattainable. Secondly, it challenges and expands the existing paradigms of evaluation theory and practice by illustrating the critical role of local perspectives in interpreting evaluation data. By adapting LLMs to reflect diverse local contexts, our proposal not only enhances the accuracy of evaluation findings but also ensures that these findings are meaningful and relevant to the communities they aim to serve.

Our work contributes to the field of evaluation by highlighting the necessity for and benefits of incorporating localized perspectives in the analysis of development interventions. This approach aligns with quality standards in evaluation theory and practice, emphasizing the importance of relevance, accuracy, and utility in evaluation outcomes. By presenting our findings, we aim to inspire further exploration into the use of localized LLMs in evaluation, advocating for a more inclusive, context-aware approach to assessing the impact of international development projects.