在输入中添加句号分隔符，让大模型更像人一样逐句思考。
Think in Sentences: Explicit Sentence Boundaries Enhance Language Model's Capabilities
- 在输入文本中显式插入句界分隔符，引导模型逐句处理。
- 在GSM8k和DROP任务上分别提升7.7%和12.5%。
- 适合关注模型推理过程可解释性的研究者。
研究人员尝试通过在上下文中插入虚拟标记来提升大语言模型（LLM）的能力，但现有方法仅关注虚拟标记本身，忽视了自然语言固有的句级结构。这一忽略尤为关键，因为LLM通过接触人类生成的文本获得语言能力，而这些文本天然具有句级结构。为此，我们提出在输入中于句界插入分隔符，不仅将虚拟标记融入上下文，还促使模型在推理时具备逐句处理的行为。我们实验了两种具体方法：（1）上下文学习，（2）监督微调，使用从7B到600B的Deepseek-V3模型。结果表明，在多个任务上均有持续提升，尤其在GSM8k上最高达7.7%、在DROP上达12.5%。此外，微调后的模型内部表示显示出明显的句意识。本工作建立了一种简单而有效的增强大模型能力的方法，为认知启发式的大模型改进提供了新方向。
原文摘要 · Abstract (English)
Researchers have explored different ways to improve large language models (LLMs)' capabilities via dummy token insertion in contexts. However, existing works focus solely on the dummy tokens themselves, but fail to leverage the inherent sentence-level structure of natural language. This is a critical oversight, as LLMs acquire linguistic capabilities through exposure to human-generated texts, which are inherently structured at the sentence level. Motivated by this gap, we propose an approach that inserts delimiters at sentence boundaries in LLM inputs, which not only integrates dummy tokens into the context, but also facilitates LLMs with sentence-by-sentence processing behavior during reasoning. Two concrete methods: (1). In-context learning and (2). Supervised fine-tuning are experimented using 7B models to 600B Deepseek-V3. Our results demonstrate consistent improvements across various tasks, with notable gains of up to 7.7\% on GSM8k and 12.5\% on DROP. Furthermore, the fine-tuned LLMs can incorporate sentence awareness evidenced by their internal representations. Our work establishes a simple yet effective technique for enhancing LLM's capabilities, offering promising directions for cognitive-inspired LLM enhancement paradigm.
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