Using LLMs to create analytical datasets: A case study of reconstructing the historical memory of Colombia
This provides a method for reconstructing historical memory in conflict-affected regions like Colombia, though it is incremental in applying existing LLM technology to a new domain.
The study tackled the lack of publicly available conflict information in Colombia by using GPT to analyze over 200,000 violence-related newspaper articles, enabling descriptive analysis and policy studies such as the relationship between violence and coca crop eradication.
Colombia has been submerged in decades of armed conflict, yet until recently, the systematic documentation of violence was not a priority for the Colombian government. This has resulted in a lack of publicly available conflict information and, consequently, a lack of historical accounts. This study contributes to Colombia's historical memory by utilizing GPT, a large language model (LLM), to read and answer questions about over 200,000 violence-related newspaper articles in Spanish. We use the resulting dataset to conduct both descriptive analysis and a study of the relationship between violence and the eradication of coca crops, offering an example of policy analyses that such data can support. Our study demonstrates how LLMs have opened new research opportunities by enabling examinations of large text corpora at a previously infeasible depth.