The impact of ChatGPT on higher education

J Dempere, K Modugu, A Hesham… - Frontiers in …, 2023 - frontiersin.org
Introduction This study explores the effects of Artificial Intelligence (AI) chatbots, with a
particular focus on OpenAI's ChatGPT, on Higher Education Institutions (HEIs). With the …

Ai alignment: A comprehensive survey

J Ji, T Qiu, B Chen, B Zhang, H Lou, K Wang… - arxiv preprint arxiv …, 2023 - arxiv.org
AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, so do risks from misalignment. To provide a comprehensive …

A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions

L Huang, W Yu, W Ma, W Zhong, Z Feng… - ACM Transactions on …, 2025 - dl.acm.org
The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), fueling a paradigm shift in information acquisition …

Detecting hallucinations in large language models using semantic entropy

S Farquhar, J Kossen, L Kuhn, Y Gal - Nature, 2024 - nature.com
Large language model (LLM) systems, such as ChatGPT or Gemini, can show impressive
reasoning and question-answering capabilities but often 'hallucinate'false outputs and …

A culturally sensitive test to evaluate nuanced gpt hallucination

TR McIntosh, T Liu, T Susnjak, P Watters… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
The generative pretrained transformer (GPT) models, renowned for generating human-like
text, occasionally produce “hallucinations”—outputs that diverge from human expectations …

Hallucinations in large multilingual translation models

NM Guerreiro, DM Alves, J Waldendorf… - Transactions of the …, 2023 - direct.mit.edu
Hallucinated translations can severely undermine and raise safety issues when machine
translation systems are deployed in the wild. Previous research on the topic focused on …

Survey of hallucination in natural language generation

Z Ji, N Lee, R Frieske, T Yu, D Su, Y Xu, E Ishii… - ACM computing …, 2023 - dl.acm.org
Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

Evaluating large language models: A comprehensive survey

Z Guo, R **, C Liu, Y Huang, D Shi, L Yu, Y Liu… - arxiv preprint arxiv …, 2023 - arxiv.org
Large language models (LLMs) have demonstrated remarkable capabilities across a broad
spectrum of tasks. They have attracted significant attention and been deployed in numerous …

Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies

L Pan, M Saxon, W Xu, D Nathani, X Wang… - Transactions of the …, 2024 - direct.mit.edu
While large language models (LLMs) have shown remarkable effectiveness in various NLP
tasks, they are still prone to issues such as hallucination, unfaithful reasoning, and toxicity. A …

Hallucination detection: Robustly discerning reliable answers in large language models

Y Chen, Q Fu, Y Yuan, Z Wen, G Fan, D Liu… - Proceedings of the …, 2023 - dl.acm.org
Large language models (LLMs) have gained widespread adoption in various natural
language processing tasks, including question answering and dialogue systems. However …