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When Linguistic and Internal Confidence Diverge in Large Language Models

Users often ask large language models (LLMs) to report how confident they are, but it is unclear whether such linguistic confidence tracks the model's internal confidence. We study this question across 8 classification tasks, 2 generation tasks and 30 models from three families. For classification, we compare linguistic confidence with logits-based confidence along three axes: association,…

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Developers that brand AI as slop will be left behind

A few days ago I shared an architectural proposal for InversifyJS on Reddit. It outlined an IPC-based type server that could help Inversify move beyond the legacy TypeScript decorator metadata APIs…

  • Developers dismissing AI advancements will fall behind in software development.
  • AI has made experimentation in software development dramatically cheaper.
  • Quality of software depends on understanding and review of AI-generated code.

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