Lightweight Adaptation for LLM-based Technical Service Agent: Latent Logic Augmentation and Robust Noise Reduction

A research team of 18 authors has published a new paper titled 'Lightweight Adaptation for LLM-based Technical Service Agent: Latent Logic Augmentation and Robust Noise Reduction' on arXiv. The work addresses a critical bottleneck in deploying large language models for complex technical support domains like cloud services. Traditional adaptation is hindered by the lack of explicit reasoning chains in human demonstrations and the high diversity of valid responses, which creates ambiguity. Furthermore, standard training methods like reinforcement learning with an LLM-as-a-judge are prohibitively expensive in terms of compute resources and time.

To solve this, the team proposes a three-part framework. First, their 'Latent Logic Augmentation' uses Planning-Aware Trajectory Modeling and Decision Reasoning Augmentation to help the AI infer the hidden decision logic behind surface-level human responses, strengthening alignment during fine-tuning. Second, 'Robust Noise Reduction' constructs a 'Multiple Ground Truths' dataset via a dual-filtering method to validate diverse correct answers, capturing semantic diversity while reducing training noise. The third and most impactful innovation is a 'Lightweight Adaptation' approach featuring a Hybrid Reward mechanism. This fuses a standard LLM-based judge with a computationally cheap, relevance-based reranker to distill high-quality reward signals for training. Empirical results on cloud service tasks show the framework achieves stability and performance gains while the Hybrid Reward mechanism delivers alignment comparable to expensive LLM-as-a-judge reinforcement learning, but with substantially reduced training time, highlighting its practical value for real-world deployment.