X. Zhang, H. Lim, K. Wang, Z. Xiao, J. Wang, K. Lee, X. Fu, Z. Qin, A Modular Data-Free Pipeline for Multi-Label Intention Recognition in Transportation Agentic AI Applications
Abstract:
In this study, a modular, data-free pipeline for multi-label intention recognization is proposed for
agentic AI applications in transportation. Unlike traditional intent recognition systems that depend
on large, annotated corpora and often struggle with fine-grained, multi-label discrimination, our
approach eliminates the need for costly data collection while enhances the accuracy of multi-label
intention understanding. Specifically, the overall pipeline, named DMTC, consists of three steps:
1. Using prompt engineering to guide large language models (LLMs) to generate diverse synthetic
queries in different transport scenarios; 2. Encoding each textual query with a Sentence-T5 model
to obtain compact semantic embeddings; 3. Training a lightweight classifier using a novel online
focal-contrastive (OFC) loss that emphasizes hard samples and maximizes inter-class separability.
The applicability of the proposed pipeline is demonstrated in an agentic AI application in the mar-
itime transportation context. Extensive experiments show that DMTC achieves a Hamming loss
of 5.35% and an AUC of 95.92%, outperforming state-of-the-art multi-label classifiers and recent
end-to-end SOTA LLM-based baselines. Further analysis reveals that Sentence-T5 embeddings
improve subset accuracy by at least 3.29% over alternative encoders, and integrating the OFC loss
yields an additional 0.98% gain compared to standard contrastive objectives. In conclusion, our
system seamlessly routes user queries to task-specific modules (e.g., ETA information, traffic risk
evaluation and other typical scenarios in transportation domain), laying the groundwork for fully
autonomous, intention-aware agents without costly manual labelling.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Singapore Maritime Institute - SMI-2022-MTP- 06
Grant Reference no. : NIL