Kavli Affiliate: Jia Liu
| First 5 Authors: Shuliang Liu, Shuliang Liu, , ,
| Summary:
The widespread deployment of large language models (LLMs) across critical
domains has amplified the societal risks posed by algorithmically generated
misinformation. Unlike traditional false content, LLM-generated misinformation
can be self-reinforcing, highly plausible, and capable of rapid propagation
across multiple languages, which traditional detection methods fail to mitigate
effectively. This paper introduces a proactive defense paradigm, shifting from
passive post hoc detection to anticipatory mitigation strategies. We propose a
Three Pillars framework: (1) Knowledge Credibility, fortifying the integrity of
training and deployed data; (2) Inference Reliability, embedding
self-corrective mechanisms during reasoning; and (3) Input Robustness,
enhancing the resilience of model interfaces against adversarial attacks.
Through a comprehensive survey of existing techniques and a comparative
meta-analysis, we demonstrate that proactive defense strategies offer up to
63% improvement over conventional methods in misinformation prevention,
despite non-trivial computational overhead and generalization challenges. We
argue that future research should focus on co-designing robust knowledge
foundations, reasoning certification, and attack-resistant interfaces to ensure
LLMs can effectively counter misinformation across varied domains.
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