Coherent Without Grounding, Grounded Without Success: Observability and Epistemic Failure
📰 ArXiv cs.AI
The assumption that coherent explanations in Large Language Models (LLMs) signal genuine understanding is challenged by the Bidirectional Coherence Paradox
Action Steps
- Recognize the Bidirectional Coherence Paradox as a challenge to the assumption that coherent explanations imply genuine understanding
- Understand how competence and grounding can dissociate and invert in LLMs
- Consider the implications of this paradox for the development and evaluation of LLMs
- Develop strategies to address the limitations of LLMs, such as incorporating additional grounding mechanisms or evaluating models based on multiple criteria
Who Needs to Know This
ML researchers and AI engineers benefit from understanding the limitations of LLMs, as it informs the development of more robust and reliable AI systems
Key Insight
💡 The Bidirectional Coherence Paradox highlights the need for a more nuanced understanding of the relationship between explanation and understanding in AI systems
Share This
💡 Coherent explanations in LLMs don't always mean genuine understanding #AI #LLMs
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