Agentic Framework for Political Biography Extraction

TL;DR

This paper introduces an agentic framework leveraging Large Language Models to automate the extraction of structured political biographies from unstructured web sources, improving accuracy and scalability over traditional methods.

Contribution

The paper presents a novel two-stage 'Synthesis-Coding' framework that enhances political biography extraction using recursive LLMs, outperforming human experts and reducing bias.

Findings

LLM coders match or outperform humans in accuracy

Agentic system synthesizes more information than Wikipedia

Synthesis stage reduces bias from long, multi-language texts

Abstract

The production of large-scale political datasets typically demands extracting structured facts from vast piles of unstructured documents or web sources, a task that traditionally relies on expensive human experts and remains prohibitively difficult to automate at scale. In this paper, we leverage Large Language Models (LLMs) to automate the extraction of multi-dimensional elite biographies, addressing a long-standing bottleneck in political science research. We propose a two-stage ``Synthesis-Coding'' framework for complex extraction task: an upstream synthesis stage that uses recursive agentic LLMs to search, filter, and curate biography from heterogeneous web sources, followed by a downstream coding stage that maps curated biography into structured dataframes. We validate this framework through three primary results. First, we demonstrate that, when given curated contexts, LLM coders match or outperform human experts in extraction accuracy. Second, we show that in web environments, the agentic system synthesizes more information from web resources than human collective intelligence (Wikipedia). Finally, we diagnosed that directly coding from long and multi-language corpora introduces bias that the synthesis stage can alleviate by curating evidence into signal-dense representations. By comprehensive evaluation, We provide a generalizable, scalable framework for building transparent and expansible large scale database in political science.