Can Memory-Augmented LLM Agents Aid Journalism in Interpreting and Framing News for Diverse Audiences?
This work addresses the challenge of making comprehensive news accessible to specialized audiences, though it is incremental as it builds on existing agent and memory-augmented LLM techniques.
The paper tackled the problem of comprehension gaps in news interpretation by diverse audiences, proposing MADES, an agent-based framework that simulates societal communication to identify misunderstandings and design supplementary material, with results showing significantly improved news understanding in agents after intervention.
Modern news is often comprehensive, weaving together information from diverse domains, including technology, finance, and agriculture. This very comprehensiveness creates a challenge for interpretation, as audiences typically possess specialized knowledge related to their expertise, age, or standpoint. Consequently, a reader might fully understand the financial implications of a story but fail to grasp or even actively misunderstand its legal or technological dimensions, resulting in critical comprehension gaps. In this work, we investigate how to identify these comprehension gaps and provide solutions to improve audiences' understanding of news content, particularly in the aspects of articles outside their primary domains of knowledge. We propose MADES, an agent-based framework designed to simulate societal communication. The framework utilizes diverse agents, each configured to represent a specific occupation or age group. Each agent is equipped with a memory system. These agents are then simulated to discuss the news. This process enables us to monitor and analyze their behavior and cognitive processes. Our findings indicate that the framework can identify confusions and misunderstandings within news content through its iterative discussion process. Based on these accurate identifications, the framework then designs supplementary material. We validated these outcomes using both statistical analysis and human evaluation, and the results show that agents exhibit significantly improved news understanding after receiving this supplementary material.