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Reinforcement Learning-driven Language Agents for Multi-domain Fact-checking and Coherent News Synthesis

Year: 2024
Type: article
Abstract: Artificial intelligence (AI) systems are increasingly being deployed to verify information and even generate news content. We try to provide a comprehensive literature review on reinforcement learning-driven language agents for multi-domain fact-checking and coherent news synthesis, with an emphasis on implications for journalism and media. We examine how large language models and other Al agents, often refined through reinforcement learning, are used to automate the identification of false claims and to produce news narratives. We find that Al-driven fact-checkers can greatly enhance the speed and scale of verification, and reinforcement learning techniques (including human feedback) have improved the factual accuracy and coherence of generated text (Roit et al., 2023). Case studies from news organizations illustrate that these tools can support human fact-checkers by flagging potential errors and synthesizing information across domains. However, challenges persist: current Al fact-checkers show inconsistent accuracy (Quelle & Bovet, 2024) and require human oversight to prevent errors or bias. Al-generated news, while increasingly coherent, may be less comprehensible to readers without editorial refinement (Thäsler-Kordonouri et al., 2024). We discuss theoretical frameworks from journalism studies to contextualize these developments, and we address uncertainties, ethical considerations, and contested perspectives. We conclude that reinforcement learning-enhanced language agents hold significant promise for journalism by augmenting fact-checking efforts and content creation, but must be integrated carefully to uphold journalistic standards of truth and trust.
DOI: 10.5281/zenodo.15258342
Language: English
Open Access status: green
OpenAlex ID: W6949528292
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