Markovian Transformers for Informative Language Modeling

• URL: http://arxiv.org/abs/2404.18988v5
• Date: Fri, 31 Jan 2025 12:28:44 GMT
• Title: Markovian Transformers for Informative Language Modeling
• Authors: Scott Viteri, Max Lamparth, Peter Chatain, Clark Barrett,
• Abstract summary: Chain-of-Thought (CoT) reasoning often fails to faithfully reflect a language model's underlying decision process. We make CoT causally essential in a "Markovian" language model, factoring next-token prediction through an intermediate CoT and training it to predict future tokens independently of the original prompt.
• Score: 0.9642500063568188
• License:
• Abstract: Chain-of-Thought (CoT) reasoning often fails to faithfully reflect a language model's underlying decision process. We address this by making CoT text causally essential in a "Markovian" language model, factoring next-token prediction through an intermediate CoT and training it to predict future tokens independently of the original prompt. We formalize this via an "informativeness" objective that quantifies how much a trained CoT improves next-token predictions over a baseline. Using policy gradient, we show that Llama 3.1 8B achieves a 33.2% absolute accuracy improvement on GSM8K. Perturbation tests confirm stronger reliance on the CoT, while cross-model transfers indicate these reasoning traces generalize across interpreters. Our approach enhances both accuracy and interpretability, potentially extending CoT reasoning to arbitrarily long contexts and diverse tasks.

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