Yubo Li

Yubo Li, Yidi Miao, Xueying Ding et al.

Large Language Models (LLMs) have shown remarkable capabilities across various tasks, but their deployment in high-stake domains requires consistent and coherent behavior across multiple rounds of user interaction. This paper introduces a comprehensive framework for evaluating and improving LLM response consistency, making three key contributions. Code and data are available at: https://github.com/yubol-bobo/MT-Consistency. First, we introduce Position-Weighted Consistency (PWC), a metric designed to capture both the importance of early-stage stability and recovery patterns in multi-turn interactions. Second, we present MT-Consistency, a carefully curated benchmark dataset spanning diverse domains and difficulty levels, specifically designed to evaluate LLM consistency under various challenging follow-up scenarios. Third, we introduce Confidence-Aware Response Generation (CARG), a framework that significantly improves response stability by explicitly integrating internal model confidence scores during the generation process. Experimental results demonstrate that CARG significantly improves response stability without sacrificing accuracy, offering a practical path toward more dependable LLM behavior in critical, real-world deployments.

Junsong Wang, Yubo Li, Zhiyong Chang et al.

Texture exists in lots of the products, such as wood, beef and compression tea. These abundant and stochastic texture patterns are significantly different between any two products. Unlike the traditional digital ID tracking, in this paper, we propose a novel approach for product traceability, which directly uses the natural texture of the product itself as the unique identifier. A texture identification based traceability system for Pu'er compression tea is developed to demonstrate the feasibility of the proposed solution. With tea-brick images collected from manufactures and individual users, a large-scale dataset has been formed to evaluate the performance of tea-brick texture verification and searching algorithm. The texture similarity approach with local feature extraction and matching achieves the verification accuracy of 99.6% and the top-1 searching accuracy of 98.9%, respectively.