Gated Relative Position Bias in Transformers

• Gated relative position bias is a phenomenon where models over-rely on the relative location of information, impacting performance in tasks like QA, token classification, and image recognition.
• Gating mechanisms such as dynamic functions, learnable scalars, and reconfigured attention masks effectively modulate positional signals to reduce spurious correlations.
• Experimental strategies like random position perturbation and content-driven gating demonstrate improved model generalizability and robustness across various transformer architectures.

Gated relative position bias refers to the phenomenon, analysis, and mitigation of models’ tendency to over-rely on the relative location of information within sequences—either text or vision—via both explicit position encoding schemes and dynamic mechanisms such as gating functions or architectural attention modifications. This bias manifests when models, especially those based on transformers, exploit spurious correlations tied to answer or feature locations rather than learning content-invariant representations. Addressing gated relative position bias is crucial for enhancing generalizability, robustness, and reliability in tasks ranging from extractive question answering (QA) and token classification to vision transformer-based image classification and long-context LLMing.

1. Definition and Manifestations of Relative Position Bias

Relative position bias occurs when a model is disproportionately influenced by the location of information relative to other key parts of the input. In extractive QA, this is precisely defined as the offset (distance) between the answer span and the nearest overlapping word in the question and context (Shinoda et al., 2022). Models often learn to exploit regularities in these positions, predicting answers based on superficial cues instead of true semantic alignment.

In token classification, position bias appears when the training corpus contains a preponderance of positive examples in early positions, degrading model performance for tokens that appear later in a sequence (Amor et al., 2023). Similarly, in vision transformers, position bias signifies the degree to which the classifier’s accuracy is enhanced by leveraging positional information about image patches, which may vary in importance depending on dataset characteristics (Bruintjes et al., 19 May 2025).

LLMs show position bias (primacy, recency, or “lost-in-the-middle” effect) when performance is sharply affected by whether relevant information appears near the start, end, or middle of their input context. This bias is tightly linked to the proportion of the context window the input occupies (Veseli et al., 10 Aug 2025).

2. Gating Mechanisms for Controlling Relative Position Bias

Gated approaches are motivated by the need to dynamically regulate how much a model relies on positional cues. In ensemble-based debiasing for QA (Shinoda et al., 2022), a gating function $g(c, q)$ is introduced, which modulates the merged probability:

$$\hat{p} = \text{softmax}( \log p + g(c, q) \cdot \log b )$$

Here, $p$ is the main model’s output, $b$ is the biased (position-dependent) model’s output, and $g(c, q)$ learns to control the influence of the position-only model based on input context and question. This function, optimized jointly during training, adaptively discounts spurious positional signals, making the system more robust to unseen answer positions.

In the PINE framework, inter-segment gating is achieved by reconfiguring the attention mask to allow bidirectional attention between document segments, counteracting order-dependent biases typically introduced by causal attention and relative positional encodings (Wang et al., 1 Jul 2024). The system decides segment ordering using similarity scores derived from attention values, effectively re-weighting segment positions in a model-derived, content-based manner.

Vision transformer classifiers use “Auto-PE,” a learnable scalar $\gamma$ to gate the impact of positional embeddings (Bruintjes et al., 19 May 2025):

$Y_i = \text{Linear}(X^p_i) + (\gamma \times P_i)$

This allows models to “unlearn” position information if it is not discriminative for the given dataset, or to amplify it when positional cues are helpful.

3. Theoretical Foundations: Causal Masking, Relative Positional Encodings, and Trade-offs

The emergence of position bias in transformers is fundamentally explained by interactions between causal masking and relative positional encodings (2502.01951). The causal mask confines each token’s attention to previous or current tokens, mathematically biasing cumulative context toward earlier sequence positions, as formalized by:

$\lim_{t\to\infty} P^{(t)}(z_i = 1 | X^{(0)}) = 1$

Relative position encodings—such as decay masks ($D_{ij} = -m(i-j)$) and RoPE—impose attention decay inversely proportional to distance, tending to favor nearby or recent tokens within a layer. However, when compounded over multiple layers, the combinatorial accumulation of the causal mask can overwhelm the local decay effect, leading to non-monotonic bias profiles. A learnable or fixed gate can intermediate between these effects, balancing layer-wise distance-based modulation with long-term cumulative primacy.

4. Measurement and Experimental Evaluation

Analyzing and quantifying gated relative position bias involves both controlled dataset construction and specialized metrics. In QA and token classification, datasets with artificially skewed position distributions are used to observe dramatic drops (up to 10–20 F1 points) for positions unseen in training, demonstrating overfitting to relative location (Shinoda et al., 2022, Amor et al., 2023).

Position-SHAP (Bruintjes et al., 19 May 2025) provides a direct diagnostic for vision transformers, isolating the contribution of position embeddings:

$$\text{Position-SHAP}(X) = \frac{\phi_p^x}{\phi_p^x + \phi_i^x}$$

In long-context LLMs, biases are quantified via relative input length ($L_\text{rel} = L_\text{input}/L_\text{max}$) and bias intensity metrics for primacy (PriMi), recency (ReCi), and the lost-in-the-middle effect (LiMi) (Veseli et al., 10 Aug 2025). Controlled numerical experiments confirm both theory and real-world phenomena like “lost-in-the-middle” and attention sinks.

5. Mitigation Strategies and Impact on Generalization

Mitigation of gated relative position bias focuses on suppressing spurious dependence on relative positions in training or inference. In QA and token classification, random position perturbation (RPP) and context perturbation (CP) broaden the effective training distributions by shifting or randomizing token positions (Amor et al., 2023):

• RPP: Random shift $\tau$ applied to each token position, forcing invariance.
• CP: Random concatenation and permutation of training samples.

In ensemble QA debiasing, the learned gate function adapts to de-emphasize the biased model when its signals are uninformative for a given context (Shinoda et al., 2022). PINE applies a gating transformation to document segment attention, yielding content-driven order-invariance (Wang et al., 1 Jul 2024).

Vision transformers leveraging Auto-PE modulate positional influence according to observed dataset bias, with empirical results showing that networks can outperform fixed PE baselines when $\gamma$ is adaptively tuned and that models benefit from “unlearning” position information in translation-invariant settings (Bruintjes et al., 19 May 2025).

6. Evolving Bias Patterns and Context Window Effects

Position bias in LLMs is context-window dependent (Veseli et al., 10 Aug 2025). When inputs occupy less than 50% of the context window, evidence of both primacy and recency biases produces a pronounced lost-in-the-middle effect. As inputs approach the maximum context length, primacy weakens sharply (“gated out”), while recency remains, leading to a pure distance-based (end-favoring) bias. These shifts in gating dynamics are significant for retrieval-augmented QA, multi-hop reasoning, and any application with extended contexts. A plausible implication is that model design, prompt engineering, and retrieval strategies should adaptively focus on signal repositioning near the end of long contexts.

7. Implications and Applications

The practical impact of controlling gated relative position bias is substantial across domains. In QA, models become more robust to unpredictable answer locations and out-of-distribution inputs. For vision transformers, dynamic gating of position embeddings yields higher accuracy on both capture-biased and translation-invariant datasets without manual hyperparameter tuning. In LLMs, bidirectional segment attention and content-driven segment ordering enable fairer and more consistent inference, especially in retrieval-heavy or evaluation tasks.

More broadly, the theoretical characterization of these biases—as a trade-off between combinatorial context accumulation and local attention decay—suggests future architectures should incorporate learnable gating functions at the attention or embedding level. This would allow for flexible adaptation to the specific positional structures of data, improving long-context understanding and sequence modeling reliability across modalities.