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About this paper
Eitan Wagner, Elisha Rosensweig, Omri Abend
The paper introduces an information-theoretic framework to argue that natural language should not entirely replace formal languages. It demonstrates a "specificity crossover" where, beyond a certain task specificity, formal languages become more efficient than natural language due to an inherent translation gap, advocating for hybrid human-AI systems.
Key insights
- Natural language is optimized for open-ended communication and low-specificity tasks, leveraging underspecification (indifferent and pragmatic) as an efficient compression mechanism.
- Formal languages excel at high-specificity tasks by ensuring full specification within restricted domains, thereby eliminating ambiguity and avoiding the "translation gap" associated with natural language.
- A fundamental "Specificity Crossover" exists, where for tasks requiring high specificity, the cost of expressing requirements in natural language, including the translation gap, exceeds the efficiency of direct formal specification.
Summary by alphaXiv.
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