Self-Elicited Knowledge Distillation

• SEKD is a technique where models use their own outputs and internal feature states as teacher signals, bypassing the need for annotated data.
• It employs iterative elicitation, dropout-induced stochastic sampling, and self-supervised contrastive learning to capture hierarchical dependencies.
• SEKD enhances model performance by improving dependency awareness, generalization to unseen taxonomies, and robustness under limited or noisy data conditions.

Self-Elicited Knowledge Distillation (SEKD) is a class of methods in which a neural model acts as its own knowledge source, using self-generated outputs or auxiliary signals to distill structured information into itself or a student model. Unlike classic knowledge distillation, where a large teacher transfers its knowledge to a smaller student using pre-existing (often ground-truth or annotated) signals, SEKD is characterized by the elicitation and transfer of intrinsic knowledge signatures—such as per-level predictions, internal feature states, or pairwise similarities—from the same model, typically without reliance on external supervision or human-annotated data. Recent work leverages SEKD to unlock hierarchical reasoning, increase dependency-awareness, or transfer self-supervised invariances across diverse architectures and modalities (Yang et al., 23 Nov 2025, Lee et al., 2022, Xu et al., 2020).

1. Core Principles and Formalization

SEKD frameworks exploit a model’s own iterative or perturbed responses as teacher signals for distillation. These self-elicited signals can arise through a variety of mechanisms:

• Iterative elicitation: The model is prompted to produce multi-step or conditional outputs (e.g., hierarchical label paths), capturing reasoning chains or dependencies (Yang et al., 23 Nov 2025).
• Stochastic elicitation: Dropout or data augmentations generate diverse model predictions, whose statistical properties (e.g., posterior distributions) are distilled into the main model (Lee et al., 2022).
• Self-supervision elicitation: The model generates auxiliary predictions (e.g., on contrastive or pretext tasks), and the structure of these outputs is transferred to the student (Xu et al., 2020).

Each SEKD instance is defined by the type of signals elicited, the distillation targets, and the absence (or minimization) of external supervision. In all cases, the student is trained to match the self-elicited teacher outputs using objectives that may involve hard labels, probability distributions, continuous features, or pairwise relationships.

2. Self-Empowering Distillation for Hierarchical Vision-Language Reasoning

In "Self-Empowering VLMs: Achieving Hierarchical Consistency via Self-Elicited Knowledge Distillation," SEKD is instantiated to address hierarchical consistency in vision–LLMs (VLMs) (Yang et al., 23 Nov 2025). The hierarchical task is formally defined as predicting a sequence of labels $y = (y^1, y^2, \ldots, y^L)$ along a taxonomy tree given an image $x$, where correctness requires each $y^l$ to be consistent with all ancestors. Path-consistency is evaluated via Hierarchical-Consistency Accuracy (HCA).

Conventional single-pass predictions fail to enforce cross-level dependencies, leading to inconsistent paths despite reasonable per-level scores. SEKD addresses this by constructing a multi-step teacher (frozen VLM) that sequentially answers each taxonomy level conditioned on its own previous outputs. The teacher emits hard labels, soft distributions, and hidden states per step. A student is then trained, in a single forward pass, to match these signals at all levels.

The distillation objectives are:

• Hard-label loss:

$$L_\mathrm{hard} = -\sum_{l=1}^L y^l \cdot \log p^{(S)}_l$$

• Soft-distribution loss:

$$L_\mathrm{soft} = \sum_{l=1}^L \mathrm{KL}(p^{(T)}_l \parallel p^{(S)}_l)$$

• Hidden-state loss:

$$L_\mathrm{hidden} = \sum_{l=1}^L \| h^{(T)}_l - W h^{(S)}_l \|_2^2$$

with overall loss $$L_\mathrm{total} = \alpha L_\mathrm{hard} + \beta L_\mathrm{soft} + \gamma L_\mathrm{hidden}$$ (typical: $\alpha=2.0$, $\beta=1.0$, $\gamma=0.5$).

This mechanism internalizes the teacher’s multi-step dependency reasoning, enabling the student to produce hierarchically consistent label paths efficiently. SEKD yields up to +29.50 percentage points over base HCA, generalizes to unseen taxonomies (e.g., Food-101 zero-shot: HCA increase from 4.15% to 42.26%), and outperforms larger base models in computational efficiency (Yang et al., 23 Nov 2025).

3. Self-Knowledge Distillation via Dropout and Posterior Matching

SEKD is also realized in the form of self-knowledge distillation using dropout-based sampling ("SD-Dropout") (Lee et al., 2022). Here, the model exposes its own uncertainty by generating multiple outputs via stochastic dropout masks applied to internal features. The posterior distributions from these samples are averaged to form an ensemble pseudo-target.

Distillation is enforced by symmetrically matching the KL divergences of the paired dropout-sampled posteriors:

$$L_\mathrm{SDD}(x; \theta) = D_\mathrm{KL}(p^u \| p^v) + D_\mathrm{KL}(p^v \| p^u)$$

where $p^u, p^v$ denote two independent dropout-perturbed output distributions.

The total loss is given by:

$$L(x, y; \theta) = L_\mathrm{CE} + \lambda_\mathrm{SDD} T^2 L_\mathrm{SDD}(x; \theta)$$

where $L_\mathrm{CE}$ is the standard cross-entropy and $T$ is the temperature.

SD-Dropout requires no architectural changes or auxiliary labels, and can be integrated into any standard classification, detection, or regularization pipeline.

4. Contrastive and Self-Supervised SEKD

A third family of SEKD approaches leverages self-supervised learning tasks to elicit richer knowledge from the teacher, as in "Knowledge Distillation Meets Self-Supervision" (Xu et al., 2020). Here, alongside ordinary classification supervision, the teacher is trained on a contrastive learning objective over pairs of augmented views.

For a sample $x$, an augmented $\tilde{x}$ is produced via random transformation $t(\cdot)$. Features are projected to an embedding space, and pairwise cosine similarities are computed and normalized (InfoNCE loss):

$$L_\mathrm{ssl} = -\sum_{i=1}^N \log \frac{\exp(A_{i,i} / \tau_s)}{\sum_{k=1}^N \exp(A_{i,k} / \tau_s)}$$

The student is distilled to match both the teacher’s soft classification outputs (on clean and augmented data) and the similarity matrices $B_t, B_s$ derived from the teacher’s and student’s embeddings, via a KL divergence regularizer:

$$L_\mathrm{ss} = -\tau_s^2 \sum_{i,j} B_t^{i,j} \log B_s^{i,j}$$

A selective transfer strategy filters out high-error similarity pairs, mitigating the effect of noisy teacher predictions.

This architecture-agnostic approach achieves state-of-the-art results, particularly when teacher and student models are structurally dissimilar or in regimes of limited or noisy labels. Gains are observed in cross-architecture distillation, few-shot learning, and robustness to label noise (Xu et al., 2020).

5. Empirical Efficacy and Scope

A selection of results demonstrating the range of SEKD is summarized in the table below:

Domain/Dataset	Baseline	SEKD/SD-Dropout	Gain / Comments
ImageNet-Animal HCA (Yang et al., 23 Nov 2025)	0.65% (base)	30.2% (SEKD student)	+29.5 pp
Food-101 Zero-Shot HCA	4.15%	42.26%	Zero-shot transfer
CIFAR-100 Top-1 (Lee et al., 2022)	74.8%	77.0%	+2.2 pp (SD-Dropout)
GQA Accuracy	+0 pp	+6.4 to +7.3 pp	Compositional VQA
Few-shot (CIFAR-100, 25%)	Lower baseline	+7 pp (SEKD vs best KD)	Robust to label scarcity

SEKD methods routinely show improvements in hierarchical consistency, generalization to new taxonomies, out-of-distribution detection, and adversarial robustness, often with reduced computational and annotation cost (Yang et al., 23 Nov 2025, Lee et al., 2022, Xu et al., 2020).

6. Analysis, Limitations, and Generalizability

SEKD techniques successfully transfer not only output probabilities but also more structured internal processes—hierarchical state, uncertainty relations, and invariances—by distilling these into a compact, dependency-aware student model. Experiments indicate that SEKD-imprinted students do not merely memorize label trees but acquire transferable multi-step reasoning skills, evidenced by generalization across taxonomies and improvements on mathematical and VQA tasks (Yang et al., 23 Nov 2025).

The absence of external supervised labels in SEKD offers scalability and application flexibility, as all signals can be mined directly from pre-trained models. Label-free operation is particularly valuable for scaling to new tasks and domains where annotation is infeasible (Yang et al., 23 Nov 2025).

A plausible implication is that SEKD’s robustness to limited or noisy data arises from the regularization effect of self-supervision or stochastic elicitation, as confirmed by performance improvements in few-shot and corrupt-label regimes (Xu et al., 2020, Lee et al., 2022).

Potential limitations include the risk of propagating model biases (as all signals originate from the same base model), and performance degradation if the teacher’s self-elicited signals are themselves inconsistent or suboptimal.

7. Relationship to Broader Distillation and Regularization Frameworks

Self-elicited approaches diverge from traditional knowledge distillation chiefly in the nature and source of their supervisory signals; SEKD is "label-free" and can distill hidden reasoning traces, not just output probabilities. Conventional knowledge distillation requires a distinct, well-trained teacher, often with access to explicit labels or massive supervision sets.

Notably, methods such as SD-Dropout demonstrate that even within a single architecture, dropout-induced ensembles provide useful self-teaching "ensembles" for effective posterior regularization (Lee et al., 2022). Contrastive and self-supervised distillation strategies reveal that auxiliary tasks can unlock additional latent knowledge, supplementing standard classification-based KD and yielding greater student flexibility, especially across architectural gaps (Xu et al., 2020).

In summary, Self-Elicited Knowledge Distillation encompasses a range of approaches where models transfer their own internal signals—generated via multi-step reasoning, stochastic perturbation, or self-supervised objectives—to enhance dependency awareness, robustness, or efficiency in student models, all without the need for explicit human annotation or external tools.