Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling
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Computer Science > Computation and Language
arXiv:2606.27705 (cs)
[Submitted on 26 Jun 2026]
Title:Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling
Authors:Changze Lv, Zhenghua Wang, Yiran Ding, Yixin Wu, Tianlong Li, Zhibo Xu, Muling Wu, Tianyuan Shi, Shizheng Li, Qi Qian, Xuanjing Huang, Xiaoqing Zheng
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Abstract:Large Language Models (LLMs) still struggle with the ``lost-in-the-middle'' problem, where critical information located in the middle of long-context inputs is often underrepresented or lost. While existing methods attempt to address this by combining multi-scale rotary position embeddings (RoPE), they typically suffer from high latency or rely on suboptimal hand-crafted scaling strategies. To overcome these limitations, we introduce a layer-specific positional embedding scaling~(LPES) method that assigns distinct scaling factors to each layer. LPES achieves a more balanced attention distribution without fine-tuning model parameters or increasing inference delay. A specially designed genetic algorithm is employed to efficiently select the optimal scaling factors for each layer by incorporating Bézier curves to significantly reduce the search space. Extensive experiments demonstrate that LPES effectively mitigates positional attention bias and delivers consistent improvements across multiple long-context benchmarks, yielding up to an $11.2$\% accuracy gain on the key-value retrieval dataset.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.27705 [cs.CL] |
| (or arXiv:2606.27705v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27705
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling, by Changze Lv and 11 other authors
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