Choice-Only Shortcuts

• Choice-only shortcuts are decision mechanisms that rely solely on available alternatives without external context or memorized mappings.
• They are applied in user interface designs like SoftCuts, minimal compromise models in decision theory, and machine learning exploitations based on spurious correlations.
• This approach enhances efficiency and accessibility but raises challenges in robustness and vulnerability to adversarial attacks in ML contexts.

A choice-only shortcut is a mechanism—spanning user interface design, decision theory, adversarial machine learning, and LLM evaluation—in which the selection or reasoning process can proceed, or appears to proceed, based exclusively on a presented set of alternatives without recourse to more contextual or underlying information. In empirical and theoretical research, the “choice-only” property can serve as a design principle for user interfaces, as a model of bounded rationality in economic and cognitive science, as an exploit in machine learning security, or as a potential confound in evaluating the true capacities of models on discriminative tasks.

1. Conceptual Foundations

A choice-only shortcut refers to a decision or selection pathway that is fully specified by the available alternatives and their direct affordances, reducing or eliminating the need for memorized mappings, additional knowledge, or deep information processing. In interactive systems, this typically means that the user or agent does not need to recall arbitrary combinations or rely on external knowledge: options are self-disclosed and can be selected via direct manipulation or by exploiting apparent regularities in the choice set alone.

Prominent instantiations include:

• Soft keyboard hotkeys (“SoftCuts”) for touch devices, where command options dynamically surface for immediate selection, guided by spatial and visual cues instead of memory (2005.13950).
• Minimal compromise models in decision theory, where the choice function depends entirely on a shortlist and a simple secondary elimination (2010.08771).
• Shortcut solutions in machine learning, where a model exploits spurious correlations present in the given alternatives (e.g., answer choices) as a path to high accuracy without broader generalization (2111.00898, 2211.16220, 2407.01992).

The core property is that all information needed to effect a plausible or optimal decision resides within the presented alternatives—sometimes by design, sometimes through exploitative learning processes.

2. Choice-Only Shortcuts in User Interface Design

Soft keyboard hotkeys (SoftCuts) represent a direct application of the choice-only shortcut principle to touch-based user interfaces (2005.13950). Traditional desktop hotkeys require memorized combinations (e.g., Ctrl+C for “Copy”), but SoftCuts generalize this mechanism for touchscreen devices:

• Dynamic Disclosure: When a modifier key is pressed, the soft keyboard layout changes contextually to display command icons/labels in place of standard characters. Thus, command selection is driven wholly by visual and spatial cues present on the interface at selection time.
• No Recall Requirement: Users need not memorize mappings; all actionable commands are “discoverable” within the layout, and the selection process is guided only by what is currently visible and selectable.
• Input Modalities: SoftCuts implement multiple input variants—User Maintained (hold modifier and tap), Once (sequential tap for modifier and command), and Swipe (press modifier, slide to key)—all fundamentally “choice-only” since no external information is required.
• Performance: Empirical studies show that the Once method allows for rapid command selection (median near 0.85 seconds, 99% accuracy for two-handed phone use), and users overwhelmingly prefer it in naturalistic scenarios, regardless of device posture or activity (sitting, standing, walking).

This interface paradigm leverages the choice-only principle as both a means of improving discoverability and reducing cognitive load, with implications for cross-device consistency and command selection efficiency.

3. Decision Theoretic Models: Minimal Compromise

In formal decision theory, the notion of a “choice-only shortcut” is encapsulated by the two-stage choice model proposed in (2010.08771). The process unfolds as follows:

1. Shortlist by Primary Preference: For a menu $A$, define a weak order $R$ (complete, transitive). The decision maker shortlists maximal elements:

$$\text{max}(A, R) = \{x \in A : \forall y \in A, x R y \}$$

1. Secondary Veto: If $|\text{max}(A, R)| = 1$, that element is chosen. Otherwise, a secondary criterion (a linear order $L$) is used only to veto the least-preferred alternative in the shortlist:

$$c(A) = \begin{cases} \text{max}(A, R), & \text{if } |\text{max}(A, R)| = 1 \ \text{max}(A, R) \setminus \{\min(\text{max}(A, R), L)\}, & \text{if } |\text{max}(A, R)| \geq 2 \end{cases}$$

Here, every decision step depends strictly and only upon the attributes of the alternatives in $A$; no extrinsic deliberation or context is invoked. The model thus serves as an explicit instantiation of a choice-only shortcut process in economic and behavioral decision-making.

This formulation satisfies Sen’s $\beta$ axiom (expansion consistency) but not the $\alpha$ (contraction consistency), capturing a plausible bounded rationality that arises from strictly acting on available options and minimal compromise.

4. Machine Learning: Shortcuts and Exploitation in Model Behavior

Shortcut learning in machine learning models describes the phenomenon in which predictive models achieve high performance by attending to superficial correlations or artifacts present in the immediately available choice set, rather than the ground-truth, generalizable relationships.

Spurious Answer-Choice Exploitation

In multiple-choice question answering (MCQA) settings, models have been shown to exploit “choice-only shortcuts”—such as learning that the answer often appears in a fixed position or correlates directly with certain option words (2211.16220):

• Behavioral Testing: Models exposed to training sets with strong choice-only cues rapidly adopt these as the dominant solution, achieving high accuracy on cases where the shortcut holds but failing on anti-shortcut instances designed to break the correlation.
• Loss Landscape and Learnability: The most learnable shortcuts correspond to flatter, deeper minima in the loss landscape, rendering them more attractive during optimization.
• Information-Theoretic Analysis: Tasks with highly learnable shortcuts have a lower minimum description length (MDL), implying that choice-only cues make the problem easier for models to “compress”—thus, a model can fit labels with reduced complexity when shortcuts are present.

Adversarial Attacks via Choice-Only Shortcuts

“Availability attacks” in supervised learning directly inject linearly separable, imperceptible perturbations into the training set, rendering poisoned datasets “unexploitable” by most machine learning models (2111.00898):

• Attack Mechanism: Perturbations are crafted so that, when added to training samples and labeled accordingly, even a simple linear classifier achieves near-perfect separation. These become a shortcut signal for the learning algorithm.
• Attack Construction: Instead of expensive optimization procedures, synthetic perturbations can be generated in seconds by sampling from high-dimensional normal distributions mapped to class-specific vertices and imposing local structure, as formalized in Algorithm 1 of (2111.00898).
• Implications: The deep model latches onto these linearly separable features (present solely in the choices), bypassing the intended, task-relevant content, leading to high training accuracy and severe generalization failure.

Setting	Shortcut Example	Model Behavior
MCQA	Answer always in first position	Model picks first choice
Availability att.	Per-sample noise, linearly separable	Network memorizes noise

A plausible implication is that any structural cue in the choice set—if sufficiently consistent—may become the model’s exclusive decision rule, regardless of the underlying data complexity.

5. Empirical Assessment in LLMs

Recent studies have directly interrogated whether the apparent success of LLMs on MCQA benchmarks is due to choice-only shortcut exploitation or genuine reasoning (2407.01992):

• Contrast Set Construction: Using a novel graph mining algorithm on UnifiedQA datasets, researchers generated large sets of question pairs where correct answers could be confused unless either the question is considered or the answer choice shortcut is strong.
• Evaluation: LLMs were probed with both full prompts (question plus choices) and choice-only prompts (choices alone). Although above-random accuracy can be achieved with just the choices, full prompt performance and ranking consistency across evaluation versus contrast sets (Kendall’s $\tau \approx 0.9$) indicate that choices-only shortcut exploitation does not drive leaderboard performance.
• Interpretation: This evidence challenges the notion that MCQA scores principally reflect choices-only shortcut learning in LLMs. The models' consistent rankings across sets designed to break naive choice-based heuristics indicate the use of question context and more substantive reasoning.

6. Challenges and Limitations

• Robustness and Generalization: Systems or models optimized for environments rich in choice-only shortcuts may demonstrate fragility or severe performance drops when those shortcuts are invalidated or intentionally obfuscated.
• Batch Selection and Sequential Tasks: Certain interface designs, such as SoftCuts’ Once method, revert the interface state after each selection—posing challenges for tasks requiring several consecutive non-text commands without further user interaction.
• Discoverability and Accessibility: The efficacy of choice-only shortcut mechanisms often depends on clear and dynamic exposure of alternatives. In keyboard-less or ad hoc interfaces, or for users unfamiliar with standard layouts, discoverability can be diminished.
• Model Overfitting: In the machine learning context, excessive reliance on choice-only features can lead networks to ignore true data structures, furthering the shortcut learning problem.

7. Future Directions

• Interface Innovation: Research continues into extending choice-only principles to varied device contexts, improving discoverability (e.g., semi-transparent overlays for soft keyboards), and enabling fast batch operations (2005.13950).
• Debiasing and Evaluation: For machine learning, future work involves balancing the construction of shortcut and anti-shortcut training examples, as well as directly considering shortcut learnability in dataset and model design (2211.16220).
• Adversarial Robustness: Addressing vulnerabilities exposed by linearly separable shortcuts in data poisoning remains an open problem for secure model deployment (2111.00898).
• Generalizability and Cognitive Modeling: The two-stage minimal compromise model offers pathways for more nuanced models of bounded rationality and hybrid preference aggregation (2010.08771).
• Automated Evaluation Sets: The graph mining methodology for constructing unbiased contrast sets is likely to see broader adoption in model evaluation frameworks to distinguish true reasoning from shortcut exploitation (2407.01992).

The paper and management of choice-only shortcuts, across domains, illuminates both opportunities for more efficient, accessible interactions and the persistent need for robustness in the face of shortcut-induced collapse—whether in user experience, systematic decision-making, or training regimes for advanced artificial intelligence systems.