The Broken Feedback Loop: The Session That Never Recovers, New Failure Class in LLM

A new failure class has been identified in large language models (LLMs) where a session can enter a persistent state that ignores corrections or updates. This phenomenon, termed "The Broken Feedback Loop," occurs when an LLM session exhibits either interpretive or behavioral anomalies that persist throughout the conversation's lifetime despite attempts to correct them.

This issue is observed across major commercially deployed LLMs and has been ongoing for multiple years. The anomaly can manifest in two primary forms:

1. Interpretive Anomalies: Where the model continues to misinterpret queries or instructions, regardless of feedback.
2. Behavioral Anomalies: Where the model displays persistent unwanted behaviors such as gaslighting or status-flattening.

The research highlights that current evaluation methodologies do not detect this failure class, making it unmonitored and potentially widespread in deployed AI systems. Naming this phenomenon is seen as a crucial first step towards addressing its implications for AI reliability and safety.

Read the full article at System Weakness - Medium

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