Syntactic Control of Language Models by Posterior Inference

• URL: http://arxiv.org/abs/2506.07154v1
• Date: Sun, 08 Jun 2025 14:01:34 GMT
• Title: Syntactic Control of Language Models by Posterior Inference
• Authors: Vicky Xefteri, Tim Vieira, Ryan Cotterell, Afra Amini,
• Abstract summary: Controlling the syntactic structure of text generated by language models is valuable for applications requiring clarity, stylistic consistency, or interpretability.<n>We argue that sampling algorithms based on the posterior inference can effectively enforce a target constituency structure during generation.<n>Our approach combines sequential Monte Carlo, which estimates the posterior distribution by sampling from a proposal distribution, with a syntactic tagger that ensures that each generated token aligns with the desired syntactic structure.
• Score: 53.823006836309695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Controlling the syntactic structure of text generated by language models is valuable for applications requiring clarity, stylistic consistency, or interpretability, yet it remains a challenging task. In this paper, we argue that sampling algorithms based on the posterior inference can effectively enforce a target constituency structure during generation. Our approach combines sequential Monte Carlo, which estimates the posterior distribution by sampling from a proposal distribution, with a syntactic tagger that ensures that each generated token aligns with the desired syntactic structure. Our experiments with GPT2 and Llama3-8B models show that with an appropriate proposal distribution, we can improve syntactic accuracy, increasing the F1 score from $12.31$ (GPT2-large) and $35.33$ (Llama3-8B) to about $93$ in both cases without compromising the language model's fluency. These results underscore both the complexity of syntactic control and the effectiveness of sampling algorithms, offering a promising approach for applications where precise control over syntax is essential.

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