@inproceedings{kim-etal-2026-layernorm,
title = "{L}ayer{N}orm Induces Recency Bias in Transformer Decoders",
author = "Kim, Junu and
Liu, Xiao and
Lin, Zhenghao and
Ji, Lei and
Gong, Yeyun and
Choi, Edward",
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.1430/",
pages = "28638--28652",
ISBN = "979-8-89176-395-1",
abstract = "Causal self-attention provides positional information to Transformer decoders. Prior work has shown that stacks of causal self-attention layers alone induce a positional bias in attention scores toward earlier tokens. However, this differs from the bias toward later tokens typically observed in Transformer decoders, known as recency bias. We address this discrepancy by analyzing the interaction between causal self-attention and other architectural components. We show that stacked causal self-attention layers combined with LayerNorm induce recency bias. Furthermore, we examine the effects of residual connections and the distribution of input token embeddings on this bias. Our results provide new theoretical insights into how positional information interacts with architectural components and suggest directions for improving positional encoding strategies."
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<abstract>Causal self-attention provides positional information to Transformer decoders. Prior work has shown that stacks of causal self-attention layers alone induce a positional bias in attention scores toward earlier tokens. However, this differs from the bias toward later tokens typically observed in Transformer decoders, known as recency bias. We address this discrepancy by analyzing the interaction between causal self-attention and other architectural components. We show that stacked causal self-attention layers combined with LayerNorm induce recency bias. Furthermore, we examine the effects of residual connections and the distribution of input token embeddings on this bias. Our results provide new theoretical insights into how positional information interacts with architectural components and suggest directions for improving positional encoding strategies.</abstract>
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%0 Conference Proceedings
%T LayerNorm Induces Recency Bias in Transformer Decoders
%A Kim, Junu
%A Liu, Xiao
%A Lin, Zhenghao
%A Ji, Lei
%A Gong, Yeyun
%A Choi, Edward
%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 kim-etal-2026-layernorm
%X Causal self-attention provides positional information to Transformer decoders. Prior work has shown that stacks of causal self-attention layers alone induce a positional bias in attention scores toward earlier tokens. However, this differs from the bias toward later tokens typically observed in Transformer decoders, known as recency bias. We address this discrepancy by analyzing the interaction between causal self-attention and other architectural components. We show that stacked causal self-attention layers combined with LayerNorm induce recency bias. Furthermore, we examine the effects of residual connections and the distribution of input token embeddings on this bias. Our results provide new theoretical insights into how positional information interacts with architectural components and suggest directions for improving positional encoding strategies.
%U https://aclanthology.org/2026.findings-acl.1430/
%P 28638-28652
Markdown (Informal)
[LayerNorm Induces Recency Bias in Transformer Decoders](https://aclanthology.org/2026.findings-acl.1430/) (Kim et al., Findings 2026)
ACL
- Junu Kim, Xiao Liu, Zhenghao Lin, Lei Ji, Yeyun Gong, and Edward Choi. 2026. LayerNorm Induces Recency Bias in Transformer Decoders. In Findings of the Association for Computational Linguistics: ACL 2026, pages 28638–28652, San Diego, California, United States. Association for Computational Linguistics.