M Jovanovic, P Voss - arXiv preprint arXiv:2404.18311, 2024 - arxiv.org
Real-time learning concerns the ability of learning systems to acquire knowledge over time, enabling their adaptation and generalization to novel tasks. It is a critical ability for …
K Zhu, J Wang, Q Zhao, R Xu, X Xie - arXiv preprint arXiv:2402.14865, 2024 - arxiv.org
Evaluation of large language models (LLMs) has raised great concerns in the community due to the issue of data contamination. Existing work designed evaluation protocols using …
Large foundation models (LFMs) are claiming incredible performances. Yet great concerns have been raised about their mythic and uninterpreted potentials not only in machine …
K Zhu, J Wang, Q Zhao, R Xu, X Xie - Forty-first International …, 2024 - openreview.net
Evaluation of large language models (LLMs) has raised great concerns in the community due to the issue of data contamination. Existing work designed evaluation protocols using …
Understanding the reasoning capabilities of Multimodal Large Language Models (MLLMs) is an important area of research. In this study, we introduce a dynamic benchmark …
Causality reveals fundamental principles behind data distributions in real-world scenarios, and the capability of large language models (LLMs) to understand causality directly impacts …
J Ni, F Xue, X Yue, Y Deng, M Shah, K Jain… - arXiv preprint arXiv …, 2024 - arxiv.org
Evaluating large language models (LLMs) is challenging. Traditional ground-truth-based benchmarks fail to capture the comprehensiveness and nuance of real-world queries, while …
Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), enabling new ways to analyze data, treat patients, and conduct …
This study intends to systematically disentangle pure logic reasoning and text understanding by investigating the contrast across abstract and contextualized logical problems from a …