concept

natural language generation

Also known as: natural language generation systems, NLG

Facts (11)

Sources

7 Benefits of Artificial Intelligence (AI) for Business - UC Online online.uc.edu University of Cincinnati Online 2 facts

claimAI can create highly personalized customer experiences by leveraging natural language generation (NLG) to answer messages and emails promptly.

claimAI-driven chatbots and automated emails, powered by natural language processing (NLP) and natural language generation (NLG), enable businesses to provide 24/7 customer service and real-time responses to social media comments.

A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Springer Nov 4, 2024 2 facts

referenceLewis M authored 'Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension', published as an arXiv preprint in 2019.

claimIntegrating LLMs with KGs improves natural language understanding and generation by allowing models to access structured data within KGs to provide accurate responses that require deep knowledge, such as specific scientific or technical details for historical events.

The Synergy of Symbolic and Connectionist AI in LLM ... arxiv.org arXiv 1 fact

claimOpenAI’s GPT-4 is an example of a Large Language Model that demonstrates unprecedented capabilities in natural language understanding and generation, exhibiting robust performance across a range of complex tasks.

Reducing hallucinations in large language models with custom ... aws.amazon.com Amazon Web Services Nov 26, 2024 1 fact

accountShayan Ray is an Applied Scientist at Amazon Web Services whose research focuses on natural language processing, natural language understanding, natural language generation, conversational AI, task-oriented dialogue systems, and LLM-based agents.

A Survey of Incorporating Psychological Theories in LLMs - arXiv arxiv.org arXiv 1 fact

claimChen et al. (2020) demonstrated that a listener's social identity influences the generation of personalized responses in natural language generation systems.

EdinburghNLP/awesome-hallucination-detection - GitHub github.com GitHub 1 fact

claimThe Survey of Hallucination in Natural Language Generation defines extrinsic hallucination as a case where the generated output cannot be verified from the source content, and intrinsic hallucination as a case where the generated output contradicts the source content.

Combining Knowledge Graphs and Large Language Models - arXiv arxiv.org arXiv Jul 9, 2024 1 fact

claimIn 2023, Hu et al. surveyed knowledge-enhanced pre-trained models with a focus on two key tasks in Natural Language Processing: Natural Language Understanding and Natural Language Generation.

Detecting and Evaluating Medical Hallucinations in Large Vision ... arxiv.org arXiv Jun 14, 2024 1 fact

referenceThe paper 'Survey of hallucination in natural language generation' by Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung, published in ACM Computing Surveys in 2023, provides a comprehensive overview of hallucination phenomena in natural language generation systems.

Understanding LLM Understanding skywritingspress.ca Skywritings Press Jun 14, 2024 1 fact

claimJackie Chi Kit Cheung's research focuses on natural language generation, automatic summarization, and integrating diverse knowledge sources into NLP systems for pragmatic and common-sense reasoning.