Balancing Engagement and Polarisation: Multi-Objective Alignment of News Content Using LLMs

We study how media firms can use LLMs to generate news content that aligns with multiple objectives – making content more engaging while maintaining a preferred level of polarisation/slant consistent with the firm’s editorial policy. Using news articles from The New York Times, we first show that more engaging human-written content tends to be more polarizing. Further, naively employing LLMs (with prompts or standard Direct Preference Optimization approaches) to generate more engaging content can also increase polarisation. This has an important managerial and policy implication: using LLMs without building in controls for limiting slant can exacerbate news media polarisation. We present a constructive solution to this problem based on the Multi-Objective Direct Preference Optimisation (MODPO) algorithm, a novel approach that integrates Direct Preference Optimisation with multi-objective optimisation techniques. We build on open-source LLMs and develop a new language model that simultaneously makes content more engaging while maintaining a preferred editorial stance. Our model achieves this by modifying content characteristics strongly associated with polarisation but that have a relatively smaller impact on engagement. Our approach and findings apply to other settings where firms seek to use LLMs for content creation to achieve multiple objectives, e.g., advertising and social media.