Think in Sentences: Explicit Sentence Boundaries Enhance Language Model's Capabilities

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

Researchers from Southern University of Science and Technology propose a novel approach to enhance large language model (LLM) capabilities by explicitly integrating sentence-level structure into inputs. This method, called "Think in Sentences," inserts task-agnostic delimiters at sentence boundaries to facilitate sentence-by-sentence processing. Two primary implementation strategies were explored: In-Context Learning (ICL) and Supervised Fine-Tuning (SFT), tested on models ranging from 7B to 600B parameters, including Qwen2-7B, LLaMA3-8B, Qwen2.5-72B, and DeepSeek-V3. The approach demonstrated consistent performance improvements, with notable gains of up to 7.7% on GSM8k and 12.5% on DROP. Ablation studies confirmed that structured, non-semantic delimiters are most effective, sentence-level segmentation is optimal, and the gains primarily stem from enhancing deliberative, multi-step reasoning rather than direct knowledge recall, as evidenced by attention map visualizations.

Key takeaway

For AI Engineers and Research Scientists optimizing LLM performance, integrating explicit sentence boundary delimiters offers a cost-neutral "free-lunch" improvement, particularly for smaller models and tasks requiring complex reasoning like reading comprehension. You should consider implementing this via In-Context Learning for quick inference-time gains or Supervised Fine-Tuning for more robust, internalized sentence awareness, ensuring your chosen delimiters are structured and non-semantic.

Key insights

Explicitly modeling sentence boundaries with delimiters enhances LLM reasoning by inducing structured, sentence-by-sentence processing.

Principles

• Structured delimiters outperform arbitrary tokens for ICL.
• Sentence-level segmentation is the optimal granularity.
• Gains arise from synergy between CoT reasoning and sentence-level inference.

Method

Insert a special delimiter token, "$x_{seg}$", at the end of each sentence in the input. Implement this via In-Context Learning (few-shot examples) or Supervised Fine-Tuning on delimiter-inserted data.

In practice

• Use structured, non-semantic delimiters like "" for best results.
• Apply sentence segmentation for tasks requiring multi-step reasoning.
• Consider fine-tuning for robust, internalized sentence awareness.

Topics

• Large Language Models
• Sentence Boundary Delimiters
• In-Context Learning
• Supervised Fine-Tuning
• Reasoning Capabilities

Code references

• CLCS-SUSTech/think-in-sentence
• segment-any-text/wtpsplit

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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• EndPrompt: Efficient Long-Context Extension via Terminal Anchoring — Sparse positional supervision with a terminal anchor can efficiently extend LLM context windows without full-length training.
• Few Shades of Supervision for Discourse Segmentation — Classical fine-tuning of LLMs is most effective for EDU segmentation in spontaneous speech.
• SeLaR: Selective Latent Reasoning in Large Language Models — SeLaR improves LLM reasoning by selectively applying soft embeddings and regularization to enhance exploration at low-confidence steps.
• ACIL: Auto Chain of Thoughts for In-Context Learning — Auto-CoT improves LLM in-context learning by automatically generating, pruning, and selecting reasoning chains.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.