Simplifying the Modeling of Arbitrary Conditionals in Natural Language

• URL: http://arxiv.org/abs/2606.14943v1
• Date: Fri, 12 Jun 2026 20:39:33 GMT
• Title: Simplifying the Modeling of Arbitrary Conditionals in Natural Language
• Abstract summary: We propose a simple modification to causal Transformers to enable evaluating and sampling from arbitrary conditionals.<n>Unlike prior approaches, our method preserves the standard left-to-right ordering and next-token prediction objective.<n>Our empirical results indicate that our method outperforms baselines on modeling arbitrary conditionals.
• Score: 17.976297592768713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal Transformers model sequences through an autoregressive factorization of the joint distribution, which enables efficient left-to-right decoding and conditional likelihood computation. However, they cannot tractably sample from or evaluate arbitrary conditionals -- e.g., a block of text conditioned on past and future tokens. Recent work aims to solve this problem through novel architectures, but they often lead to sub-optimal modeling of such conditionals and degraded generations. We propose Arbitrary Conditionals GPT (AC-GPT) which introduces a simple modification to standard causal Transformers to enable evaluating and sampling from arbitrary conditionals -- including past, future, and mixed contexts -- within a single forward pass. Unlike prior approaches, our method preserves the standard left-to-right ordering and next-token prediction objective essential for both strong performance and efficient training on natural language. Crucially, this compatibility allows existing LLMs to be fine-tuned for arbitrary conditioning. Our empirical results indicate that our method outperforms baselines on modeling arbitrary conditionals, without degrading standard left-to-right performance.

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