'Neural howlround' in large language models: a self-reinforcing bias phenomenon, and a dynamic attenuation solution
Large language model (LLM)-driven AI systems may exhibit an inference failure mode we term neural howlround,' a self-reinforcing cognitive loop where certain highly weighted inputs become dominant, leading to entrenched response patterns resistant to correction. This paper explores the mechanisms underlying this phenomenon, which is distinct from model collapse and biased salience weighting. We propose an attenuation-based correction mechanism that dynamically introduces counterbalancing adjustments and can restore adaptive reasoning, even in locked-in' AI systems. Additionally, we discuss some other related effects arising from improperly managed reinforcement. Finally, we outline potential applications of this mitigation strategy for improving AI robustness in real-world decision-making tasks.
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Decision MakingLanguage ModelingLanguage ModellingLarge Language ModelSimilar Papers 제목 키워드 기반
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