Semantic Dependency Injection for Scoped Meaning Control

Semantic Dependency Injection for Scoped Meaning Control

— A Programming Technique for Mitigating Misreadings and Enforcing Semantic Constraints in LLMs —

This proposal presents a “semantic control structure” for GPT and similar LLMs,
and is released in a non-commercial, open format for reinterpretation and use by researchers and developers.

The author does not intend to monetize or commercialize this model.
Collaborations on prototyping and implementation are welcome.

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Abstract

This document proposes a new control method for large language models (LLMs) in natural language processing,
called Semantic Dependency Injection (Semantic DI), which injects meaning definitions into limited namespaces (scopes),
thereby preventing LLMs from misinterpreting or hallucinating concepts and terms.

Unlike conventional dependency injection (DI) which operates at the code level,
Semantic DI injects meaning definitions directly into the LLM's internal "semantic recognition scope".

This method is especially useful in AI-support development environments using models like ChatGPT,
where complex, cross-domain contexts can cause semantic collapse or misinterpretation.
Semantic DI provides a framework for scoped meaning control and structured contextual constraints.

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1. Introduction

LLMs generally generate outputs via probabilistic predictions.
Therefore, in contexts involving advanced terminology or metaphors,
LLMs often misread or misinterpret terms with unintended meanings.

For instance, a phrase like “thermal vibration in Lab space” may be misunderstood as a physical phenomenon,
leading the model into thermodynamic or diffusion models erroneously.

Such misreadings are not failures of model capability, but of contextual control design.
This paper proposes a method for design-time semantic control of LLMs
by defining meanings and injecting them into localized scopes.

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2. Definition and Structure of Semantic DI

Semantic Dependency Injection (Semantic DI) is defined as:

A technique for injecting meaning definitions, metaphorical roles, and usage constraints
for specific vocabulary or concepts into limited semantic scopes (e.g., namespaces, documents, sections).

2.1 Core Components

• namespace: The valid range for meaning definitions (e.g., Dithering.SemanticModeling)
• aliases: Vocabulary to be redefined and their intended interpretations
• scope: The contextual application range (e.g., per canvas, per prompt)
• type: Semantic attribute (e.g., physical, metaphor, statistical, visual)
• note: Additional instructions or restrictions to prevent misinterpretation

2.2 Example Definition (JSON)

{
  "namespace": "SemanticDithering.PerceptionPhysics",
  "aliases": {
    "thermal vibration in Lab": {
      "type": "metaphor",
      "meaning": "Probabilistic fluctuation in labeling diffusion process (non-physical)",
      "note": "This is not a physical phenomenon but a semantic model instability"
    },
    "semantic field": {
      "type": "region",
      "meaning": "Label-valid region in Lab space (f(x)>0)",
      "note": "Used to define probabilistic category boundaries"
    }
  },
  "scope": "canvas",
  "binding": "manual"
}

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3. Applications and Benefits

Semantic DI provides several implementation-level benefits:

Problem	Semantic DI Response
Misinterpretation of technical terms	Define meanings explicitly and limit their scopes
Misreading metaphors as physical	Use type: "metaphor" to clearly mark non-physical context
Meaning scope pollution	Maintain locality of interpretation to prevent cross-context leakage
AI “hallucinatory overintegration”	Enable design-time convergence of semantic nodes

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4. Namespace Linking and Human Responsibility

Semantic DI intentionally avoids automatic inter-scope linking by LLMs.

Reasons include:

• Meaning reinterpretation is highly context-dependent; automatic linking often leads to misreadings
• Semantic consistency across namespaces should be designed explicitly by humans
• Prohibiting unintended cross-references helps isolate hallucinations and prevent conceptual infection

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5. Summary and Future Applications

Semantic DI is a foundational design technique for teaching LLMs “what means what”
and enforcing educational control over their semantic behavior.

Future applications include:

• LLMs with scoped semantic control in academic, legal, and technical contexts
• Modular, prompt-based AI systems with structured syntax constraints
• DSLs (domain-specific languages) supporting Semantic DI-based meaning control

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Supplement: Why Semantic DI Is Necessary

In LLM-based coding assistants like ChatGPT,
hallucination often arises when changing specs or mixing contexts—
not because of weakness in the model, but because semantic consistency is not enforced structurally.

As corrections accumulate, LLMs may build consistent-seeming but false belief structures.

Instead of fixing broken contexts, we must redesign the context structure itself.
Semantic DI addresses this by redefining AI’s meaning recognition via scoped namespaces.

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Implementation Notes: Semantic Namespaces and Human Responsibility

Semantic DI treats namespace as the unit of semantic control, similar to how programming languages use namespaces.
Each namespace acts as a capsule of meaning, defining vocabulary, expressions, and metaphors
that apply only within that context.

Structural Image:

• Each namespace is scoped to a domain or context
• Expressions carry meaning only within that scope
• Cross-scope binding is disallowed by default and must be manually defined

This ensures semantic integrity, preventing unwanted interpretation or hallucination propagation.

Example:

{
  "namespace": "DesignAI.Materials",
  "aliases": {
    "weight": {
      "type": "physical",
      "meaning": "Weight based on mass × gravity",
      "note": "Distinguished from metaphorical or psychological 'weight'"
    }
  },
  "scope": "design-spec",
  "binding": "manual"
}

Thus, controlling and localizing vocabulary meanings
is the first step toward treating LLMs as semantically typed programming languages.