Zobrazeno 1 - 10
of 22 518
pro vyhledávání: '"Language as Performance"'
Autor:
Safavi-Naini, Seyed Amir Ahmad, Ali, Shuhaib, Shahab, Omer, Shahhoseini, Zahra, Savage, Thomas, Rafiee, Sara, Samaan, Jamil S, Shabeeb, Reem Al, Ladak, Farah, Yang, Jamie O, Echavarria, Juan, Babar, Sumbal, Shaukat, Aasma, Margolis, Samuel, Tatonetti, Nicholas P, Nadkarni, Girish, Kurdi, Bara El, Soroush, Ali
Background and Aims: This study evaluates the medical reasoning performance of large language models (LLMs) and vision language models (VLMs) in gastroenterology. Methods: We used 300 gastroenterology board exam-style multiple-choice questions, 138 o
Externí odkaz:
http://arxiv.org/abs/2409.00084
Automated fact-checking is a key strategy to overcome the spread of COVID-19 misinformation on the internet. These systems typically leverage deep learning approaches through Natural Language Inference (NLI) to verify the truthfulness of information
Externí odkaz:
http://arxiv.org/abs/2409.00061
Autor:
Wang, Jiaqi, Jiang, Hanqi, Liu, Yiheng, Ma, Chong, Zhang, Xu, Pan, Yi, Liu, Mengyuan, Gu, Peiran, Xia, Sichen, Li, Wenjun, Zhang, Yutong, Wu, Zihao, Liu, Zhengliang, Zhong, Tianyang, Ge, Bao, Zhang, Tuo, Qiang, Ning, Hu, Xintao, Jiang, Xi, Zhang, Xin, Zhang, Wei, Shen, Dinggang, Liu, Tianming, Zhang, Shu
In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (AI) systems. Designed to seamlessly integrate diverse data types-inclu
Externí odkaz:
http://arxiv.org/abs/2408.01319
Large Language Models (LLMs) have demonstrated remarkable capabilities in executing tasks based on natural language queries. However, these models, trained on curated datasets, inherently embody biases ranging from racial to national and gender biase
Externí odkaz:
http://arxiv.org/abs/2407.17688
Autor:
Chen, Yuyan, Wen, Zhihao, Fan, Ge, Chen, Zhengyu, Wu, Wei, Liu, Dayiheng, Li, Zhixu, Liu, Bang, Xiao, Yanghua
Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community. The existing research primarily emphasizes the importance of adapting prompts to specific tasks,
Externí odkaz:
http://arxiv.org/abs/2407.04118
Understanding how language model performance varies with scale is critical to benchmark and algorithm development. Scaling laws are one approach to building this understanding, but the requirement of training models across many different scales has l
Externí odkaz:
http://arxiv.org/abs/2405.10938
Large-scale multilingual Pretrained Language Models (mPLMs) yield impressive performance on cross-language tasks, yet significant performance disparities exist across different languages within the same mPLM. Previous studies endeavored to narrow the
Externí odkaz:
http://arxiv.org/abs/2404.08491
Large Language Models (LLMs) excel at providing information acquired during pretraining on large-scale corpora and following instructions through user prompts. This study investigates whether the quality of LLM responses varies depending on the demog
Externí odkaz:
http://arxiv.org/abs/2406.17385
Autor:
Anugraha, David, Winata, Genta Indra, Li, Chenyue, Irawan, Patrick Amadeus, Lee, En-Shiun Annie
Performance prediction is a method to estimate the performance of Language Models (LMs) on various Natural Language Processing (NLP) tasks, mitigating computational costs associated with model capacity and data for fine-tuning. Our paper introduces P
Externí odkaz:
http://arxiv.org/abs/2406.09334
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant memories from an external database. However, existing RAG methods typically organize all memories in a whole database, potentially limiting focus on cru
Externí odkaz:
http://arxiv.org/abs/2405.16420