@inproceedings{wang-etal-2026-mitigating-position,
title = "Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling",
author = "Wang, Zhenghua and
Ding, Yiran and
Lv, Changze and
Wu, Yixin and
Li, Tianlong and
Xu, Zhibo and
Wu, Muling and
Shi, Tianyuan and
Li, Shizheng and
Qian, Qi and
Huang, Xuanjing and
Zheng, Xiaoqing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1059/",
pages = "21084--21098",
ISBN = "979-8-89176-395-1",
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 B'{e}zier 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."
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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 B’ezier 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.</abstract>
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%0 Conference Proceedings
%T Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling
%A Wang, Zhenghua
%A Ding, Yiran
%A Lv, Changze
%A Wu, Yixin
%A Li, Tianlong
%A Xu, Zhibo
%A Wu, Muling
%A Shi, Tianyuan
%A Li, Shizheng
%A Qian, Qi
%A Huang, Xuanjing
%A Zheng, Xiaoqing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-mitigating-position
%X 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 B’ezier 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.
%U https://aclanthology.org/2026.findings-acl.1059/
%P 21084-21098
Markdown (Informal)
[Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling](https://aclanthology.org/2026.findings-acl.1059/) (Wang et al., Findings 2026)
ACL
- Zhenghua Wang, Yiran Ding, Changze Lv, Yixin Wu, Tianlong Li, Zhibo Xu, Muling Wu, Tianyuan Shi, Shizheng Li, Qi Qian, Xuanjing Huang, and Xiaoqing Zheng. 2026. Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling. In Findings of the Association for Computational Linguistics: ACL 2026, pages 21084–21098, San Diego, California, United States. Association for Computational Linguistics.