Relevance Requires Non‑Markovian Conditioning: Empirical Proof that Goal‑Directed Generation Demands Bidirectional Context
Relevance Requires Non‑Markovian Conditioning: Empirical Proof that Goal‑Directed Generation Demands Bidirectional Context
We demonstrate that the ability to produce relevant (goal‑directed) text is architecturally distinct from the ability to produce coherent (statistically fluent) text. Using a controlled text‑generation simulation, we show that a unidirectional Markovian language model achieves 0% relevance on novel compositional goals, while bidirectional and multidirectional models (Seq2Seq LSTM and Transformer) achieve 100% relevance. The clean 0% vs. 100% split provides the first empirical proof that relevance requires non‑Markovian conditioning, and that coherence and relevance are fundamentally different dimensions of text quality.
Transformer, goal‑directed generation, language models, AI safety, Markov chain, non‑Markovian, text generation, relevance, bidirectional LSTM, coherence
Transformer, goal‑directed generation, language models, AI safety, Markov chain, non‑Markovian, text generation, relevance, bidirectional LSTM, coherence
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