Self-Verbalization in AI & Cognitive Science

• Self-verbalization is the process of transforming latent internal states into explicit linguistic or visual representations that enhance introspection and communication.
• It employs methodologies from reflective human diagrams to automated language generation, integrating controlled natural language and visualization techniques.
• Applications span ontology engineering, QA systems, and multimodal agents, driving improved model calibration, interpretability, and autonomous reasoning.

Self-verbalization refers to the process by which individuals, systems, or models convert internal states—such as preferences, knowledge, or reasoning steps—into explicit linguistic or visual representations. This externalization, whether performed by humans or artificial agents, serves introspective, communicative, interpretative, and even control-oriented functions. In contemporary computational research, self-verbalization encompasses diverse methodologies across cognitive science, information visualization, ontology engineering, language modeling, and multimodal AI, often providing both insights into internal mechanisms and practical utility for interaction, learning, and autonomous improvement.

1. Core Principles and Definitions

Self-verbalization is grounded in the transformation of latent or unconscious content into overt, shareable forms. The process may involve:

• Reflection: Internal review or self-examination of thoughts and preferences.
• Visualization: Diagrammatic or graphical encoding of relationships and strengths, such as “preference diagrams” encoding subject-artwork relationships as graphs (0803.4074).
• Controlled Natural Language (CNL): Converting formal axioms to readable, contextual explanations for ontology elements (Liepiņš et al., 2016).
• Machine-generated Language: Automating verbalization in natural language for question answering, relation extraction, or reasoning steps (Biswas et al., 2021, Sainz et al., 2021, Weng et al., 2022).
• Explicit Confidence and Verification: Integrating scalar self-confidence scores and prolonging reasoning chains contingent on model certainty (Jang et al., 4 Jun 2025).

Key mathematical and algorithmic constructs in self-verbalization include:

• Preference weight functions for cluster analysis.
• Layer-wise mapping from latent inference representations to output tokens in LMs, demonstrating separation of inference and verbalization functions (Tao et al., 12 Oct 2024).

2. Methodologies of Self-Verbalization

Human-centric Approaches

Reflective Visualization (0803.4074) exemplifies a structured approach for surfacing unconscious preferences by orchestrating reflection, diagram construction (via k-medoids clustering with Jaccard similarity), and group verbalization. The methodology identifies primary and secondary clusters and “switch objects” in preference graphs, subsequently facilitating nuanced verbalization through group discussion, especially for weak preferences:

$$J(n_i, n_j) = \frac{F(n_i \cap n_j)}{F(n_i \cup n_j)}$$

$$W(n_{pin}, n_j) = \frac{B(n_j \in d_i)}{\sum_{l=0}^{|d|} B(n_j \in d_l)}$$

Adjustable granularity (number of clusters) enables the adaptation to individual introspective resolution.

Visualization-enhanced Verbalization

Ontology visualization systems augment graphical elements with contextual verbalizations, constructing function-based mappings from OWL axioms to CNL statements (Liepiņš et al., 2016):

$$V: \{a_1, a_2, \dots, a_n\} \rightarrow \{s_1, s_2, \dots, s_n\}$$

Interactive selection enables users to obtain precise natural language explanations for each diagram element, improving understanding and debugging.

Data-driven and Template-driven Machine Verbalization

Verbalized Answer Generation: Datasets such as VANiLLa (Biswas et al., 2021) focus on KGQA, transforming retrieved answers and their context into syntactically and semantically enriched sentences. The transformation function:

$A_V = f(Q_{nl}, A_R)$

Template-Led Label Verbalization: In relation extraction, hand-crafted verbalization templates coupled with textual entailment engines recast supervised classification as an entailment problem (Sainz et al., 2021):

$f = \arg\max Pr(x, xe_1, xe_2)$

$$Pr(x, xe_1, xe_2) = d_r(e_1, e_2) \cdot \max_{t \in T_{(r)}} [PNLI(x, VERBALIZE(t, xe_1, xe_2))]$$

This reduces annotation cost in zero- and few-shot setups.

Self-Verification and Confidence-driven Reasoning

Chain-of-thought LLMs that integrate self-verification mechanisms perform backward checking via condition masking or true/false verification, aggregating correctness scores over repeated samples (Weng et al., 2022):

$$Score_{y} = \sum_{p=1}^{P} \mathbb{I} \{LLM_p(X + f_{y}) \text{ matches original } f\}$$

Scalar self-confidence scores, when output by LMs, trigger extended reasoning on low-confidence questions, manifesting emergent self-checking and calibration without explicit reasoning supervision (Jang et al., 4 Jun 2025):

$$\hat{p}(q) = \frac{1}{K} \sum_{i=1}^{K} \mathbb{I}[a^{(i)} = a^*] \quad c = \lfloor 100 \cdot \hat{p}(q) \rfloor$$

3. Applications and Functional Roles

Self-verbalization techniques facilitate a broad spectrum of tasks:

Modality	Method	Functional Role
Human Preference	Reflective diagrams, granularity tuning (0803.4074)	Introspection, awareness, communication
Ontology Engineering	Interactive CNL pop-ups (Liepiņš et al., 2016)	Documentation, learning, debugging
QA Systems	Contextual answer generation (Biswas et al., 2021)	Validation, human-like responses
Relation Extraction	Template verbalization + entailment (Sainz et al., 2021)	Annotation minimization, robustness
Multimodal Agents	Textual trajectory and observation prompts (Schumann et al., 2023, Yang et al., 2023)	Integrated reasoning, embodiment
Generative Models	Self-verification, confidence scores (Weng et al., 2022, Jang et al., 4 Jun 2025)	Calibration, safety, interpretability
Vocabulary Expansion	Neologism learning, plug-in evaluation (Hewitt et al., 9 Oct 2025)	Model controllability, interpretability

4. Internal Mechanisms and Causal Structure

Recent research demonstrates that self-verbalization in LLMs is implemented as compositionally sequential functions that can be distinctly localized. During in-context learning, the inference function deduces the answer, and the verbalization function maps internal representations to output label space (Tao et al., 12 Oct 2024):

$$\text{IntInv}(M, x, x', \mathfrak{z}) \equiv M_{\mathfrak{z} \leftarrow M_{x'}(\mathfrak{z})}(x)$$

Experiments reveal that the inference function is invariant to label space remapping, while the verbalization function operates in later layers, suggesting opportunities for modular manipulation and interpretability.

5. Extensions to Multimodal and Embodied Systems

Self-verbalization has been extended to settings involving complex sensory and environmental data:

• In VLN systems, visual observations and actions are verbalized as sequential prompts, allowing LLM agents to reason over concatenated textual summaries of sensorimotor experience (Schumann et al., 2023).
• Inner monologue frameworks simulate cognitive questioning processes, exchanging queries and answers between a “Reasoner” and “Observer” to build enriched dialogue histories and improved problem-solving (Yang et al., 2023).

In virtual reality, real-time speech is transcribed and propagated to LLMs, whose output is informationally visualized as interactive 3D “idea balloons,” facilitating ideation and reflection (Xing et al., 23 Sep 2024).

6. Control, Alignment, and Interpretability

Neologism learning presents a direct avenue for both model interpretability and controllability. By enriching the embedding matrix with new tokens representing target concepts and observing the model’s natural language self-verbalization of their function, LLMs can both explain and steer behavioral traits (Hewitt et al., 9 Oct 2025). Plug-in evaluation—with machine-only synonyms—validates the causal connection between learned vocabulary and modeled behaviors.

Self-verbalization supports interpretability and alignment by making implicit mechanisms explicit, guiding further research into robust, controllable, and transparent AI.

7. Future Perspectives and Research Directions

The trend toward self-verbalization suggests expansions in several directions:

• Hybrid systems binding retrieval, parametric, and self-verbalized knowledge in flexible RAG architectures (Wu et al., 1 Apr 2025).
• Autonomous model evolution leveraging internal feedback loops for robust continual learning (Lu et al., 2023).
• Dynamic reward structures and reinforcement schemes where internal verbalized signals inform reasoning and policy optimization (Yang et al., 2023).
• General-purpose frameworks for integrating verbalization processes across multimodal input types, further blurring boundaries between perception, reasoning, and expression (e.g., VR-based ideation).

In summary, self-verbalization serves as a universal technique—encompassing meta-cognitive, communicative, control, and evolutionary functions—underpinning many recent advances in both human-computer interaction and autonomous AI. Experimental, computational, and theoretical work continues to refine the mechanisms and applications of self-verbalization, promoting deeper understanding of system introspection, agent autonomy, and interactive interpretability across domains.