Plan-Guided Text Simplification with Extended Contexts

In this paper, we investigate the impact of increasing context lengths (one to five paragraphs) on plan-following accuracy in plan-guided text simplification. Plan-guided models simplify text according to sentence-level operation labels such as copy, rephrase, split, and delete. Previous work fine-tunes BART with target reading-level and sentence-level operation tokens to perform this task. We find that BART’s plan-following accuracy on Newsela-auto drops significantly as context increases from one to five paragraphs. This means that the model becomes less reliable with longer contexts, and the quality of its outputs decreases. To address this, we propose replacing the fine-tuned BART models with a prompting-based approach using instruction-tuned Qwen models. We find that this approach not only maintains robust plan-following across all context lengths, but even at the longest context length still exceeds BART’s performance at the shortest. We further provide ablations on model size and model family, showing that a minimum model capacity is required for the approach to work and that it transfers across LLM families.