Omkar2118/datasets huggingface dataset:
KIEval

KIEval provides a robust framework for dynamic, interactive evaluation of large language models, reducing the impact of data contamination and offering deeper insights into a model's true capabilities. It shifts the focus from static evaluation to a more comprehensive assessment of knowledge understanding and application. How KIEval Works:

Dynamic Interactions: KIEval introduces an "interactor" model that engages in multi-round dialogues with the evaluated model. Each round generates new, deeper questions based on previous responses, testing the model's knowledge application and coherence.

Evaluation Process: An initial question from a high-quality dataset starts the dialogue. The "interactor" then generates follow-up questions to probe deeper. An "evaluator" model assesses responses for relevance, coherence, and logic.

Advantages: This method reduces the impact of data contamination and comprehensively evaluates the model's abilities beyond simple pattern matching.

Introducing paper: KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models
ESSVP
Full name: ESSVP dataset

Provide:

A multimodal ESSVP dataset is built with 224×224 size RGB images and 59-channel EEG data. This dataset contains more than 13,000 paired samples of EEG and images, presenting promising prospects for the further development of multimodal learning methods in target detection tasks.

Introducing paper: Adaptive Modality Balanced Online Knowledge Distillation for Brain-Eye-Computer based Dim Object Detection