AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes Linear Semantic Control (LiSeCo), a lightweight, gradient-free intervention that dynamically steers trajectories away from regions corresponding to undesired meanings in natural language generation in latent space.
We explore intrinsic dimension (ID) of LLM representations as a marker of linguistic complexity. Specifically, we test whether ID differences across model layers reflect well-known complexity contrasts established in (psycho)linguistics: coordination vs. subordination, right-branching vs. center-embedding, and unambigu...
Modeling language use as a joint distribution over meanings, contexts, and utterances, upper bounds are derived on the probability that a decoder recovers a speaker's intended meaning from a representation of the utterance.
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