Spurious Features in Prompt Design

• Spurious features in prompt design are non-causal prompt attributes that, despite being irrelevant to the task, induce model bias and unpredictable behavior.
• Empirical studies reveal that formatting and contextual variations can shift model accuracy by up to 76 points, highlighting the impact of these features.
• Mitigation techniques such as FormatSpread, InSPEcT, and SEraser offer practical guidelines for robust prompt design in LLMs, VLMs, and multimodal systems.

Spurious features in prompt design arise when models leverage attributes of prompts that correlate with labels or outcomes in the training data, yet are unrelated to the core semantics or intended task. These features—ranging from formatting artifacts in text prompts to contextual pixels in vision-LLMs—induce brittle behavior, bias, or poor generalization when distribution shifts or adversarial settings obscure the original spurious signal. Understanding how spurious features emerge, their formal properties, and how to systematically detect and suppress them is a critical concern for robust prompt engineering in LLMs, vision-LLMs (VLMs), and multimodal systems.

1. Definitions and Taxonomies of Spurious Features

Spurious features are non-causal or incidental prompt attributes that influence model responses despite being semantically orthogonal to the user’s intent. In LLM prompt design, these include formatting defects (punctuation, delimiters, role-tagging, position), irrelevant context, and stylistic quirks that should have no bearing on output, yet cause substantial shifts in behavior (Tian et al., 17 Sep 2025, Sclar et al., 2023, Ismithdeen et al., 4 Sep 2025). In vision-language settings, spurious features encompass background pixels, co-occurring objects, or surface-level visual/text patterns that models use as "decision shortcuts" (Ma et al., 1 Mar 2024, Jiang et al., 11 Mar 2025).

Spurious features are formally characterized by the property that two prompts $p$ and $p'$ differing only on a feature $f$ (where $f$ is not semantic for $T$, the intended task) induce different model outputs: $f(p) \ne f(p')$ and $M(p) \ne M(p')$, while both prompts represent the same task intent (Tian et al., 17 Sep 2025). In continuous prompt regimes, they are latent signals in soft prompt embeddings $P_n^{cont}$ that, despite offering predictive power during tuning, induce model bias or shortcuts unrelated to label-causal features (Ramati et al., 15 Oct 2024, Rahman et al., 26 Jun 2025).

Taxonomic organization delineates:

• Invariant (causal) features: attributes stable across context shifts, causally linked to label (Ma et al., 1 Mar 2024, Rahman et al., 26 Jun 2025).
• Spurious (decision shortcut) features: correlates in data that fail under OOD evaluation (e.g., background water for "waterbird," formatting delimiters in few-shot prompts) (Ma et al., 1 Mar 2024, Ismithdeen et al., 4 Sep 2025, Sclar et al., 2023).

Prompt defect taxonomies place spurious features primarily under Structure/Formatting, Input & Content (irrelevant context), Context & Memory, and Performance/Efficiency categories (Tian et al., 17 Sep 2025).

2. Empirical Manifestations and Impact

Empirical analyses across NLP and multimodal tasks reveal spurious features as a dominant factor in prompt sensitivity:

• LLM sensitivity: In benchmarked LLMs, semantically equivalent prompt formats (e.g., punctuation, newlines, choice notation) induce spread in accuracy up to 76 points (LLaMA-2-13B), even when model size or shot count increases (Sclar et al., 2023). Proprietary LMMs (GPT-4o, Gemini 1.5 Pro) exhibit greater sensitivity to non-semantic prompt variations than open-source LMMs, with prompt swings of up to 15 points in MCQA (Ismithdeen et al., 4 Sep 2025).
• Multimodal MCQA: Promptception catalogs 61 prompt types (formatting, role-play, answer handling, etc.), showing that categories such as role-play, penalty/incentive framing, and ambiguous/statistical cues can degrade or boost accuracy by up to 40 points, despite unchanged task content (Ismithdeen et al., 4 Sep 2025).
• Vision-LLMs: CLIP and similar VLMs attend to spurious cues (e.g., scene background) leading to marked drops in OOD and worst-group accuracy. SEraser demonstrates that removing reliance on such features recovers robust performance, closing group gaps by 50–75% (Ma et al., 1 Mar 2024): | Dataset | Vanilla Worst-Group | SEraser Worst-Group | Gap Reduction | |-----------|--------------------|---------------------|---------------| | Waterbirds| 40.0 | 65.3 | ~50% | | CelebA | 23.9 | 88.4 | ~95% | | MetaShift | 90.8 | 93.8 | up to 3.7 pts |
• Continuous Prompts: InSPEcT exposes latent textual correlates (e.g., "not," "never"→contradiction bias in SNLI) in soft prompts; these features correlate with label bias and can be quantitatively linked to prediction drift via predictive bias metrics $\Delta(c, f)$ (Ramati et al., 15 Oct 2024).

3. Formal Algorithmic Approaches for Detection and Quantification

Text Domain

• FormatSpread for LLM sensitivity: FormatSpread algorithm systematically samples the space $\mathcal{F}$ of plausible prompt formats, measuring min-max accuracy over $k$ samples via Thompson sampling (Sclar et al., 2023):

$$\text{FormatSpread}(T, M, k) = \left[ \min_{f \in \mathcal{F}} \operatorname{Acc}(M, P_f, T),\ \max_{f \in \mathcal{F}} \operatorname{Acc}(M, P_f, T) \right]$$

• Promptception metrics: Defines trimmed mean $\tilde\mu$ over 61 prompt types, prompt-specific deviation $\Delta_{\text{prompt}}$, and sensitivity score $S_{\text{model}}$ as the std. dev. across prompt accuracies (Ismithdeen et al., 4 Sep 2025).

Continuous Prompts

• InSPEcT: Dissects soft prompt embeddings by patching their hidden representations into a target prompt, decoding to reveal surface textual correlates (spurious features). Predictive bias is measured as $$\Delta(c, f) = \rho_{pred}(c|S_f) - \rho_{true}(c|S_f)$$ (Ramati et al., 15 Oct 2024).

Vision-Language and Multimodal

• SEraser / Test-Time Prompt Tuning: Jointly maximizes output entropy on background ($x_{ir}$) and minimizes entropy on the foreground ($x_{re}$):

$$\mathcal{L}_{ir}(\text{Pr}) = \operatorname{KL}(P(\cdot|x_{ir};\text{Pr}) \Vert q),\quad \mathcal{L}_{re}(\text{Pr}) = -H(P(\cdot|x_{re};\text{Pr}))$$

$$\text{Pr}^* = \arg\min_{\text{Pr}}\ [\mathcal{L}_{ir} + \mathcal{L}_{re}]$$

This forces the prompt to encode invariant object features, reducing spurious alignment with backgrounds (Ma et al., 1 Mar 2024).

• DiMPLe: Separates invariant and spurious subspaces via four projections ($\phi_v$, $\psi_v$, $\phi_t$, $\psi_t$), aligns only invariants cross-modally through a contrastive loss, regularizes spurious features to uniformity, and minimizes mutual information between invariant and spurious subspaces (Rahman et al., 26 Jun 2025).
• DPT (Debiased Prompt Tuning): Automatically pseudo-labels spurious group attributes using CLIP’s zero-shot capabilities, forms groups, and dynamically reweights their contributions to the prompt tuning objective to suppress spurious-induced accuracy gaps (Jiang et al., 11 Mar 2025).

4. Root Causes and Theoretical Insights

The overreliance on spurious features is rooted in the empirical risk minimization paradigm, where models optimize for prediction accuracy over the training distribution without causal constraints. Spurious correlations present in co-occurrence statistics are adopted as shortcuts when they suffice to maximize the objective. In prompt-based inference, this amplifies model brittleness:

• LLMs' tokenization and attention architectures encode formatting and context cues as latent control signals, leading to unpredictable output variance and fragile system behavior (Tian et al., 17 Sep 2025, Sclar et al., 2023).
• In VLMs, the alignment objectives and backbone pretraining encourage exploitation of easily discriminative but non-causal attributes (e.g., background color), insufficiently penalizing spurious alignments unless OOD or counterfactual examples are available (Ma et al., 1 Mar 2024, Rahman et al., 26 Jun 2025).

The successful mitigation strategies, such as entropy maximization on background cues or mutual-information minimization between invariants and spurious signals, theoretically approximate the goals of invariant risk minimization and causal feature alignment (Ma et al., 1 Mar 2024, Rahman et al., 26 Jun 2025).

5. Mitigation and Robust Prompt Design Principles

Mitigating the impact of spurious features requires both automated and process-oriented interventions:

Method	Domain	Key Principle
Prompt Linters	LLMs	Detect / fix formatting, role-tag errors
Output Enforcement	LLMs	Enforce schemas (JSON, XML)
Entropy Regularization	VLMs / continuous	Suppress spurious distributions on confounders
Group Reweighting	VLMs	Upweight worst-group, pseudo-attribute split
Feature Disentanglement	Multimodal / VLMs	Isolate invariants, penalize spurious projections
Prompt Ensembles	Continuous prompts	Dilute spurious signals by combining complementary prompts
Data Augmentation	All	Inject counterexamples to decouple spurious semant.

Contemporary prompting guidelines in both LLM and multimodal regimes consistently recommend:

• Using explicit, neutral, and organized structure with well-defined headers and output formats (e.g., “Question:… Options:… Answer:…” with “(A)” labels) (Ismithdeen et al., 4 Sep 2025).
• Minimizing reliance on chain-of-thought, answer-handling, incentive, or roleplay modifiers unless empirically verified for the relevant model class (Ismithdeen et al., 4 Sep 2025).
• Early-stage prompt inspection using interpretability tools like InSPEcT or confidence visualizations to preemptively reveal unintentional biases (Ramati et al., 15 Oct 2024, Ma et al., 1 Mar 2024).
• Balancing the tuning objectives (contrastive, spurious regularization, mutual information) toward robust OOD and worst-group accuracy, as in DiMPLe (Rahman et al., 26 Jun 2025).

Empirical evidence shows that simple prompt templates with neutral formatting nearly eliminate the differential effects of spurious prompt features when compared to variants embedding penalties, statistical probabilities, persona instructions, or complex structured formats (Ismithdeen et al., 4 Sep 2025).

6. Open Challenges and Future Directions

The identification and suppression of spurious features in prompt design remains an open area. Challenges include:

• Automated detection: Constructing static analyzers or model-driven auditors to flag formatting fragility and non-semantic artifacts (Tian et al., 17 Sep 2025).
• Robust evaluation: Replacing single-format accuracy with metrics such as FormatSpread or prompt sensitivity intervals to properly characterize model reliability (Sclar et al., 2023, Ismithdeen et al., 4 Sep 2025).
• Generalization to undiscovered spurious attributes: Moving beyond known-label splits or human-designed pseudo-groups to surface unknown vulnerable features, especially in high-dimensional multimodal spaces (Jiang et al., 11 Mar 2025).
• Prompt repair and dynamic adaptation: Leveraging LLMs for self-healing prompt generation or on-the-fly context summarization (Tian et al., 17 Sep 2025).
• Causal disentanglement in learned prompts: Further decomposing prompt and feature spaces via learned invariance and spurious heads, potentially leveraging advanced independence estimators (e.g. MINE, HSIC) (Rahman et al., 26 Jun 2025).
• Fairness and safety: Evaluating models for worst-case prompt-induced bias, critical for socially sensitive applications, remains under-standardized (Sclar et al., 2023).

Continued research in robust prompt engineering must unify advances in explanation, detection, and loss-based suppression of spurious features across modalities, with quantifiable gains in OOD robustness and systematic reporting of model fragility to ensure dependable foundation model deployment (Tian et al., 17 Sep 2025, Ma et al., 1 Mar 2024, Rahman et al., 26 Jun 2025, Ismithdeen et al., 4 Sep 2025, Sclar et al., 2023, Ramati et al., 15 Oct 2024, Jiang et al., 11 Mar 2025).