LLMs generate 'fluent nonsense' when reasoning outside their training zone
3 days ago
- #Chain-of-Thought
- #AI Research
- #LLM Limitations
- Arizona State University study suggests Chain-of-Thought (CoT) reasoning in LLMs may be a 'brittle mirage' rather than genuine intelligence.
- CoT prompting shows impressive results but often reveals logical inconsistencies, relying on surface-level semantics rather than deep reasoning.
- LLMs struggle to generalize reasoning abilities, performing well only when test inputs resemble training data.
- Researchers propose that CoT is sophisticated pattern matching, not reasoning, bound by training data patterns.
- Performance collapses when tested outside the training data distribution, across task, length, and format dimensions.
- Fine-tuning can quickly fix specific failures but doesn't address the core lack of abstract reasoning.
- Enterprises should guard against over-reliance on CoT, prioritize out-of-distribution testing, and recognize fine-tuning as a temporary fix.
- Targeted testing and fine-tuning can align LLMs with specific enterprise tasks, ensuring reliability within narrow domains.