Unverified50% confidenceOpinionExact time
The capabilities of large language models depend greatly on the scale of training data, and the relatively limited volume of open-source Clojure code means models may generate Clojure code less accurately than mainstream languages.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/8/2026
First Seen
Valid until: 1/6/2027
Sources
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Related Claims
UnverifiedThe structural weakness of large language models is that they optimize for 'code that looks correct' rather than 'code that meets the requirements of the current stage,' and they replicate high-frequency patterns from training data.75% similarUnverifiedThe researchers acknowledge that the model still falls far short of classical language models trained on trillions of tokens on text tasks.73% similarVerifiedLarge language models have two inherent weaknesses: their knowledge is cut off at training time and they cannot obtain the latest information, and they can only generate text and cannot directly perform actions.72% similarUnverifiedAfter large language models exceed a critical scale, new capabilities that were not explicitly taught during the training phase emerge, such as multi-step reasoning, cross-lingual analogy, and tool-use planning.72% similar
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