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The case for zero-error horizons in trustworthy LLMs
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Overview
Zero-Error Horizon (ZEH) is proposed as a metric for evaluating the maximum range of error-free performance in large language models (LLMs). An evaluation of GPT-5.2's ZEH reveals significant insights into its limitations, including its inability to accurately count to five.
Summary from arxiv.org.
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Hacker News · arxiv.org
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- Praise: The article emphasizes the importance of quantifying reliability for specific tasks, which can help users better understand LLM limitations and avoid overgeneralization.
- Praise: There is a recognition that LLMs excel at generating scripts and automation, suggesting their strength lies in assisting with complex tasks rather than executing them flawlessly.
- Concern: Many users mistakenly believe LLMs possess human-like reasoning and learning capabilities, leading to unrealistic expectations about their performance in tasks like accounting and coding.
- Concern: The concept of zero-error horizons may not be applicable to LLMs, as these models operate more like Kahneman's System 1, which limits their reliability in complex evaluations.