Shikun Zhang

Unsupervised Out-of-Domain Detection via Pre-trained Transformers

2021 · arXiv (Cornell University)

Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs. Those out-of-domain inputs can lead to unpredictable outputs and sometimes catastrophic safety issues. Prior studies on out-of-domain detection require in-domain …

Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models

2024 · arXiv (Cornell University)

Large Vision Language Models exhibit remarkable capabilities but struggle with hallucinations inconsistencies between images and their descriptions. Previous hallucination evaluation studies on LVLMs have identified hallucinations in terms of objects, attributes, and relations but overlooked …

Labels Need Prompts Too: Mask Matching for Natural Language Understanding Tasks

2024 · Proceedings of the AAAI Conference on Artificial Intelligence

Textual label names (descriptions) are typically semantically rich in many natural language understanding (NLU) tasks. In this paper, we incorporate the prompting methodology, which is widely used to enrich model input, into the label side …

RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation

2024 · arXiv (Cornell University)

Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. However, even the most advanced LLMs face challenges such as hallucinations and real-time updating of their knowledge. Current research addresses this bottleneck …