5 accepted papers
Task-oriented Dialog systems (ToD) are essential in automating user interactions, but their complex design and dynamic nature make evaluation particularly challenging. Current evaluation methodologies heavily depend on human annotators, which can be inefficient, subjective, and expensive to scale. T…
Effective customer support requires domain-specific solutions tailored to users’ issues. However, LLMs like ChatGPT, while excelling in open-domain tasks, often face challenges such as hallucinations, lack of domain compliance, and imprecise solutions when applied to specialized contexts. RAG-based…
Vision Language Models (VLMs) have achieved significant advancements in complex visual understanding tasks. However, VLMs are prone to hallucinations—generating outputs that lack alignment with visual content. This paper addresses hallucination detection in VLMs by leveraging the visual grounding in…
General vision-language models (VLMs) trained on web data struggle to understand and converse about real-world e-commerce product images. We propose a cost-efficient approach for collecting training data to train a generative VLM for e-commerce product images. The key idea is to leverage large-scale…
Binary classification typically involves predicting the label of an instance based on whether the model score for the positive class exceeds a threshold chosen based on the application requirements (e.g., maximizing recall for a precision bound). However, model scores are often not aligned with true…