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    <abstract>Decoder-only Large Language Models (LLMs) have demonstrated exceptional performance in language generation, exhibiting broad capabilities across various tasks. However, the application to label-sensitive language understanding tasks remains challenging due to the limitations of their autoregressive architecture, which restricts the sharing of token information within a sentence. In this paper, we address the Multi-Intent Detection (MID) task and introduce MIDLM, a bidirectional LLM framework that incorporates intent number detection and multi-intent selection. This framework allows autoregressive LLMs to leverage bidirectional information awareness through post-training, eliminating the need for training the models from scratch. Comprehensive evaluations across 8 datasets show that MIDLM consistently outperforms both existing vanilla models and pretrained baselines, demonstrating its superior performance in the MID task.</abstract>
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%0 Conference Proceedings
%T MIDLM: Multi-Intent Detection with Bidirectional Large Language Models
%A Yin, Shangjian
%A Huang, Peijie
%A Xu, Yuhong
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F yin-etal-2025-midlm
%X Decoder-only Large Language Models (LLMs) have demonstrated exceptional performance in language generation, exhibiting broad capabilities across various tasks. However, the application to label-sensitive language understanding tasks remains challenging due to the limitations of their autoregressive architecture, which restricts the sharing of token information within a sentence. In this paper, we address the Multi-Intent Detection (MID) task and introduce MIDLM, a bidirectional LLM framework that incorporates intent number detection and multi-intent selection. This framework allows autoregressive LLMs to leverage bidirectional information awareness through post-training, eliminating the need for training the models from scratch. Comprehensive evaluations across 8 datasets show that MIDLM consistently outperforms both existing vanilla models and pretrained baselines, demonstrating its superior performance in the MID task.
%U https://aclanthology.org/2025.coling-main.179/
%P 2616-2625

Markdown (Informal)

[MIDLM: Multi-Intent Detection with Bidirectional Large Language Models](https://aclanthology.org/2025.coling-main.179/) (Yin et al., COLING 2025)

• MIDLM: Multi-Intent Detection with Bidirectional Large Language Models (Yin et al., COLING 2025)

ACL

• Shangjian Yin, Peijie Huang, and Yuhong Xu. 2025. MIDLM: Multi-Intent Detection with Bidirectional Large Language Models. In Proceedings of the 31st International Conference on Computational Linguistics, pages 2616–2625, Abu Dhabi, UAE. Association for Computational Linguistics.