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arxiv: 2604.10135 · v2 · submitted 2026-04-11 · 💻 cs.CL · cs.AI

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

Zhichen Liu , Yongyuan Li , Yang Xu This is my paper

Pith reviewed 2026-05-10 15:59 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords sentence boundarieslarge language modelsin-context learningsupervised fine-tuningreasoning taskssentence awarenessGSM8KDROP
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The pith

Inserting delimiters at sentence boundaries improves large language models' reasoning on math and reading tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that natural language's built-in sentence structure can be made explicit to LLMs by adding boundary markers to their inputs. This guides the models to handle reasoning one sentence at a time instead of as unbroken text. Tests with models from 7 billion to 600 billion parameters show steady gains on tasks that require step-by-step work. Fine-tuning with the markers also produces internal model states that reflect sentence-level awareness.

Core claim

By placing delimiters at the ends of sentences in LLM inputs, models adopt sentence-by-sentence processing during reasoning. The approach is tested via in-context learning and supervised fine-tuning across model sizes from 7B to 600B. It produces consistent gains on multiple benchmarks, including up to 7.7 percent on GSM8K and 12.5 percent on DROP. Fine-tuned models further show sentence awareness through changes in their internal representations.

What carries the argument

Sentence-boundary delimiters placed in input contexts to encourage sentence-by-sentence processing behavior.

If this is right

  • Gains appear on arithmetic reasoning and reading comprehension benchmarks.
  • Both in-context learning and supervised fine-tuning benefit from the added sentence structure.
  • Fine-tuned models develop measurable sentence awareness inside their representations.
  • The method works across model scales up to 600 billion parameters.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Marking other units such as clauses or paragraphs might produce similar structured-processing benefits.
  • The technique could be layered with existing prompting methods like chain-of-thought for combined effects.
  • It suggests that explicit cues can compensate for the lack of sentence structure in typical LLM training data.

Load-bearing premise

The gains come from the sentence-boundary structure itself rather than from simply adding any extra tokens or changing input length or format.

What would settle it

An experiment that inserts the same number of delimiters at random non-sentence positions and finds no comparable performance gains on GSM8K or DROP.

Figures

Figures reproduced from arXiv: 2604.10135 by Yang Xu, Yongyuan Li, Zhichen Liu.

Figure 1
Figure 1. Figure 1: Overview of Sentence-Level Inference: We insert delimiters at sentence boundaries to enable LLMs to “pause and integrate context” during inference. Two approaches are proposed: (1) In-Context Learning (ICL): LLMs infer with delimiter placement from exemplars in long contexts; (2) Supervised Fine-Tuning (SFT): LLMs learn sentence-segmented patterns via delimiter-inserted training data. For Llama3-8B-Instruc… view at source ↗
Figure 2
Figure 2. Figure 2: The distributions of sentence lengths and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Performance of different delimiter choices in ICL across three datasets. More structured delimiters could [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Sentence-level vs. random delimiter place [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Relative attention scores for different delim [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Attention map of Llama3-8b-seg [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Attention map of Qwen2-7b-Instruct. The segmentation token we used is “ [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that inserting explicit delimiters at sentence boundaries in LLM inputs improves reasoning performance by enabling sentence-by-sentence processing. It evaluates this via in-context learning and supervised fine-tuning on models from 7B to 600B parameters, reporting gains of up to 7.7% on GSM8k and 12.5% on DROP, plus evidence of sentence awareness in the internal representations of fine-tuned models.

Significance. If the gains are shown to arise specifically from sentence structure rather than generic token insertion or length changes, the work provides a simple, cognitively motivated technique for enhancing LLM capabilities on reasoning tasks. The breadth of model scales tested is a strength, as is the dual evaluation of ICL and SFT regimes.

major comments (2)
  1. [§4] §4 (Results): The reported improvements on GSM8k and DROP lack an ablation that inserts an equivalent number of delimiters at random positions or word boundaries while holding total token count and input length fixed. Without this control, the central claim that gains derive from sentence-level structure (as opposed to any added delimiters or altered input statistics) cannot be isolated from the dummy-token baselines the paper itself contrasts against.
  2. [§3 and §4] §3 (Methods) and §4: No details are provided on exact prompt templates, baseline configurations, statistical significance testing, or variance across runs. These omissions make it impossible to verify that the 7.7% and 12.5% gains are robust and attributable to the proposed sentence-boundary intervention.
minor comments (2)
  1. [Abstract and §1] The abstract and introduction use inconsistent terminology for model sizes (e.g., '7B models to 600B Deepseek-V3'); clarify the exact model list and whether all sizes received identical treatment.
  2. [§5] Figure captions and axis labels in the internal-representation analysis section should explicitly state what metric or layer is being visualized to allow readers to assess the 'sentence awareness' claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the isolation of sentence-boundary effects and improve reproducibility. We address each point below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§4] §4 (Results): The reported improvements on GSM8k and DROP lack an ablation that inserts an equivalent number of delimiters at random positions or word boundaries while holding total token count and input length fixed. Without this control, the central claim that gains derive from sentence-level structure (as opposed to any added delimiters or altered input statistics) cannot be isolated from the dummy-token baselines the paper itself contrasts against.

    Authors: We agree that an ablation inserting delimiters at random positions or word boundaries (with fixed count and token length) would more rigorously isolate sentence structure from generic delimiter effects. Our manuscript already contrasts against dummy-token baselines, but these do not fully address random or word-boundary controls. In the revised version, we will add these experiments across the reported model scales and tasks to confirm that gains stem specifically from sentence boundaries. revision: yes

  2. Referee: [§3 and §4] §3 (Methods) and §4: No details are provided on exact prompt templates, baseline configurations, statistical significance testing, or variance across runs. These omissions make it impossible to verify that the 7.7% and 12.5% gains are robust and attributable to the proposed sentence-boundary intervention.

    Authors: We acknowledge these omissions limit verifiability. The revised manuscript will include the full prompt templates for in-context learning and supervised fine-tuning, precise baseline configurations, results of statistical significance tests (e.g., paired t-tests), and variance (standard deviations) across multiple runs with varied seeds to demonstrate robustness of the gains. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical results with no derivation chain

full rationale

The paper reports experimental outcomes from inserting sentence-boundary delimiters via in-context learning and supervised fine-tuning on models from 7B to 600B parameters, measuring gains on GSM8k (up to 7.7%) and DROP (up to 12.5%). No equations, fitted parameters, or self-referential definitions appear in the provided text. Claims rest on direct performance measurements rather than any reduction of a 'prediction' to an input quantity by construction. Self-citations, if present, are not load-bearing for the central empirical findings, which remain falsifiable against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the empirical observation that sentence-boundary delimiters improve benchmark scores; it assumes standard LLM training dynamics and the linguistic premise that human text is structured at the sentence level, with no free parameters, invented entities, or additional axioms required beyond those.

axioms (1)
  • domain assumption LLMs acquire linguistic capabilities through exposure to human-generated texts which are inherently structured at the sentence level.
    Invoked in the abstract as the motivation for leveraging sentence boundaries.

pith-pipeline@v0.9.0 · 5499 in / 1280 out tokens · 55216 ms · 2026-05-10T15:59:04.424200+00:00 · methodology

discussion (0)

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Reference graph

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