Binxia Xu

Zengguang Hao, Jie Zhang, Binxia Xu et al.

Intent detection and identification from multi-turn dialogue has become a widely explored technique in conversational agents, for example, voice assistants and intelligent customer services. The conventional approaches typically cast the intent mining process as a classification task. Although neural classifiers have proven adept at such classification tasks, the issue of neural network models often impedes their practical deployment in real-world settings. We present a novel graph-based multi-turn dialogue system called , which identifies a user's intent by identifying intent elements and a standard query from a dynamically constructed and extensible intent graph using reinforcement learning. In addition, we provide visualization components to monitor the immediate reasoning path for each turn of a dialogue, which greatly facilitates further improvement of the system.

Binxia Xu, Antonis Bikakis, Daniel Onah et al.

Given that AI systems are set to play a pivotal role in future decision-making processes, their trustworthiness and reliability are of critical concern. Due to their scale and complexity, modern AI systems resist direct interpretation, and alternative ways are needed to establish trust in those systems, and determine how well they align with human values. We argue that good measures of the information processing similarities between AI and humans, may be able to achieve these same ends. While Representational alignment (RA) approaches measure similarity between the internal states of two systems, the associated data can be expensive and difficult to collect for human systems. In contrast, Behavioural alignment (BA) comparisons are cheaper and easier, but questions remain as to their sensitivity and reliability. We propose two new behavioural alignment metrics misclassification agreement which measures the similarity between the errors of two systems on the same instances, and class-level error similarity which measures the similarity between the error distributions of two systems. We show that our metrics correlate well with RA metrics, and provide complementary information to another BA metric, within a range of domains, and set the scene for a new approach to value alignment.

Binxia Xu, Xiaoliang Luo, Luke Dickens et al.

Determining whether AI systems process information similarly to humans is central to cognitive science and trustworthy AI. While modern AI models match human accuracy on standard tasks, such parity does not guarantee that their underlying decision-making strategies are aligned with human information processing. Assessing performance using i) error alignment metrics to compare how humans and models fail, and ii) using distorted, or otherwise more challenging, stimuli, provides a viable pathway toward a finer characterization of model-human alignment. However, existing out-of-distribution (OOD) analyses for challenging stimuli are limited due to methodological choices: they define OOD shift relative to model training data or use arbitrary distortion-specific parameters with little correspondence to human perception, hindering principled comparisons. We propose a human-centred framework that redefines the degree of OOD as a spectrum of human perceptual difficulty. By quantifying how much a collection of stimuli deviates from an undistorted reference set based on human accuracy, we construct an OOD spectrum and identify four distinct regimes of perceptual challenge. This approach enables principled model-human comparisons at calibrated difficulty levels. We apply this framework to object recognition and reveal unique, regime-dependent model-human alignment rankings and profiles across deep learning architectures. Vision-language models are the most consistently human aligned across near- and far-OOD conditions, but CNNs are more aligned than ViTs for near-OOD and ViTs are more aligned than CNNs for far-OOD conditions. Our work demonstrates the critical importance of accounting for cross-condition differences such as perceptual difficulty for a principled assessment of model-human alignment.