Structure of Thought prompting and T2S-Bench benchmark improve LLM text processing through explicit intermediate representations.
Think about how human handles complex reading tasks: marking key points, inferring their relationships, and structuring information to guide understanding and responses. Likewise, can a large language model benefit from text structure to enhance text-processing performance? To explore it, in this work, we first introduce Structure of Thought (SoT), a prompting technique that explicitly guides models to construct intermediate text structures, consistently boosting performance across eight tasks and three model families. Building upon this insight, we present T2S-Bench, the first benchmark designed to evaluate and improve text-to-structure capabilities of models. T2S-Bench includes 1.8K samples across 6 scientific domains and 32 structural types, rigorously constructed to ensure accuracy, fairness, and quality. Evaluation on 45 mainstream models reveals substantial improvement potential: the average accuracy on the multi-hop reasoning task is only 52.1%, and even the most advanced model achieves 58.1% node accuracy in end-to-end extraction. Furthermore, on Qwen2.5-7B-Instruct, SoT alone yields an average +5.7% improvement across eight diverse text-processing tasks, and fine-tuning on T2S-Bench further increases this gain to +8.6%. These results highlight the value of explicit text structuring and the complementary contributions of SoT and T2S-Bench. Dataset and eval code have been released at https://t2s-bench.github.io/T2S-Bench-Page/.
Structure of Thought prompting consistently improves LLM performance by 5-10% across diverse text-processing tasks.
T2S-Bench provides the first comprehensive benchmark with 1.8K samples across 32 structural types for evaluating text-to-structure capabilities.
Fine-tuning on T2S-Bench increases downstream task performance by up to 8.6%, demonstrating structured text processing value.
Even state-of-the-art models achieve only 58.1% node accuracy on end-to-end extraction tasks, indicating substantial remaining challenges.
Current approaches remain task-specific and heavily reliant on particular input structures, limiting generalization across diverse text tasks.
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