Large Language Models (LLMs) have demonstrated impressive capabilities in language generation and general task performance. However, their application to spoken language understanding (SLU) remains challenging, particularly for token-level tasks, where the autoregressive nature of LLMs often leads to misalignment issues. They also struggle to capture nuanced interrelations in semantic-level tasks through direct fine-tuning alone. To address these challenges, we propose the Entity-level Language Model (ECLM) framework, which reformulates slot-filling as an entity recognition task and introduces a novel concept, Chain of Intent, to enable step-by-step multi-intent recognition. Experimental results show that ECLM significantly outperforms strong baselines such as Uni-MIS, achieving gains of 3.7% on MixATIS and 3.1% on MixSNIPS. Compared to standard supervised fine-tuning of LLMs, ECLM further achieves improvements of 8.5% and 21.2% on these datasets, respectively. Our code is available at https://github.com/SJY8460/ECLM.

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.

“近年来,多意图口语理解(SLU)已经成为自然语言处理领域的研究热点。当前先进的多意图SLU模型采用图-交互式框架进行联合多意图识别和槽位填充,能够有效地捕捉到词元级槽位填充任务的细粒度意图信息,取得了良好的性能。但是,它忽略了联合作用下的意图所包含的丰富信息,没有充分利用多意图信息对槽填充任务进行指引。为此,本文提出了一种基于多意图融合框架(MIFF)的联合多意图识别和槽填充框架,使得模型能够在准确地识别不同意图的同时,利用意图信息为槽填充任务提供更充分的指引。我们在MixATIS和MixSNIPS两个公共数据集上进行了实验,结果表明,我们的模型在性能和效率方面均超过了当前最先进的方法,同时能够有效从单领域数据集泛化到多领域数据集上。”