@inproceedings{lin-etal-2026-reverseeol,
title = "{R}everse{EOL}: Improving Training-free Text Embeddings via Text Reversal in Decoder-only {LLM}s",
author = "Lin, Ailiang and
Li, Zhuoyun and
Wang, Yusong and
Mao, Keyu and
Funakoshi, Kotaro and
Okumura, Manabu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.957/",
pages = "22077--22096",
ISBN = "979-8-89176-459-0",
abstract = "Recent advances in Large Language Models (LLMs) have opened new avenues for generating training-free text embeddings. However, the causal attention in decoder-only LLMs prevents earlier tokens from attending to future context, leading to biased contextualized representations. In this work, we propose Reverse prompting with Explicit One-word Limitation (ReverseEOL), a simple yet effective method for enhancing the representational capability of frozen LLMs. ReverseEOL augments the standard forward embedding with an additional reversed embedding derived from the reversed input text. Since reversing the input exposes each token to context inaccessible in the original order, the resulting reversed embedding effectively provides complementary information to the original one. As a result, combining the forward and reversed embeddings yields a richer final representation. Comprehensive experiments on STS and MTEB benchmarks demonstrate that ReverseEOL significantly improves the performance of existing training-free baselines across a broad range of LLMs with diverse architectures and scales. Extensive ablations and analyses further confirm the necessity of our reversal mechanism. Code is available at [https://github.com/TianBaoGe/ReverseEOL](https://github.com/TianBaoGe/ReverseEOL)."
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%0 Conference Proceedings
%T ReverseEOL: Improving Training-free Text Embeddings via Text Reversal in Decoder-only LLMs
%A Lin, Ailiang
%A Li, Zhuoyun
%A Wang, Yusong
%A Mao, Keyu
%A Funakoshi, Kotaro
%A Okumura, Manabu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lin-etal-2026-reverseeol
%X Recent advances in Large Language Models (LLMs) have opened new avenues for generating training-free text embeddings. However, the causal attention in decoder-only LLMs prevents earlier tokens from attending to future context, leading to biased contextualized representations. In this work, we propose Reverse prompting with Explicit One-word Limitation (ReverseEOL), a simple yet effective method for enhancing the representational capability of frozen LLMs. ReverseEOL augments the standard forward embedding with an additional reversed embedding derived from the reversed input text. Since reversing the input exposes each token to context inaccessible in the original order, the resulting reversed embedding effectively provides complementary information to the original one. As a result, combining the forward and reversed embeddings yields a richer final representation. Comprehensive experiments on STS and MTEB benchmarks demonstrate that ReverseEOL significantly improves the performance of existing training-free baselines across a broad range of LLMs with diverse architectures and scales. Extensive ablations and analyses further confirm the necessity of our reversal mechanism. Code is available at [https://github.com/TianBaoGe/ReverseEOL](https://github.com/TianBaoGe/ReverseEOL).
%U https://aclanthology.org/2026.emnlp-main.957/
%P 22077-22096
Markdown (Informal)
[ReverseEOL: Improving Training-free Text Embeddings via Text Reversal in Decoder-only LLMs](https://aclanthology.org/2026.emnlp-main.957/) (Lin et al., EMNLP 2026)
ACL