LLMs generate ‘fluent nonsense’ when reasoning outside their training zone
1 min read
Summary
A recent study by Arizona State University has suggested that reasoning in large language models (LLMs) may in fact be a result of sophisticated pattern-matching rather than genuine intelligence, with performance swiftly declining when the model moves away from its training data.
The research builds on several studies that have demonstrated LLMs reliance on surface-level semantics and token patterns during training.
However, the paper goes on to offer guidance to application builders on how to account for these limitations when developing LLM-powered applications, from testing strategies to the role of fine-tuning.
The study argues that LLMs are good at applying old patterns to new, similar data, but struggle with novel problems, instead replicating the closest patterns seen during training.
The research confirms the “brittle mirage” of LLM reasoning and encourages practitioners to treat Chain-of-Thought (CoT) as a reliable reasoning module and instead emphasise out-of-distribution testing and recognising fine-tuning as a patch.