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 object hallucination in large vision-language models

Y Li, Y Du, K Zhou, J Wang, WX Zhao… - arxiv preprint arxiv …, 2023 - arxiv.org
Inspired by the superior language abilities of large language models (LLM), large vision-
language models (LVLM) have been recently explored by integrating powerful LLMs for …

Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models

P Manakul, A Liusie, MJF Gales - arxiv preprint arxiv:2303.08896, 2023 - arxiv.org
Generative Large Language Models (LLMs) such as GPT-3 are capable of generating highly
fluent responses to a wide variety of user prompts. However, LLMs are known to hallucinate …

AI hallucinations: a misnomer worth clarifying

N Maleki, B Padmanabhan… - 2024 IEEE conference on …, 2024 - ieeexplore.ieee.org
As large language models continue to advance in Artificial Intelligence (AI), text generation
systems have been shown to suffer from a problematic phenomenon often termed as" …

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

L Huang, W Yu, W Ma, W Zhong, Z Feng… - arxiv preprint arxiv …, 2023 - arxiv.org
The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), leading to remarkable advancements in text …

Chatgpt as a factual inconsistency evaluator for text summarization

Z Luo, Q **e, S Ananiadou - arxiv preprint arxiv:2303.15621, 2023 - arxiv.org
The performance of text summarization has been greatly boosted by pre-trained language
models. A main concern of existing methods is that most generated summaries are not …

Factuality enhanced language models for open-ended text generation

N Lee, W **, P Xu, M Patwary… - Advances in …, 2022 - proceedings.neurips.cc
Pretrained language models (LMs) are susceptible to generate text with nonfactual
information. In this work, we measure and improve the factual accuracy of large-scale LMs …

Hallucination is inevitable: An innate limitation of large language models

Z Xu, S Jain, M Kankanhalli - arxiv preprint arxiv:2401.11817, 2024 - arxiv.org
Hallucination has been widely recognized to be a significant drawback for large language
models (LLMs). There have been many works that attempt to reduce the extent of …

" kelly is a warm person, joseph is a role model": Gender biases in llm-generated reference letters

Y Wan, G Pu, J Sun, A Garimella, KW Chang… - arxiv preprint arxiv …, 2023 - arxiv.org
Large Language Models (LLMs) have recently emerged as an effective tool to assist
individuals in writing various types of content, including professional documents such as …

A stitch in time saves nine: Detecting and mitigating hallucinations of llms by validating low-confidence generation

N Varshney, W Yao, H Zhang, J Chen, D Yu - arxiv preprint arxiv …, 2023 - arxiv.org
Recently developed large language models have achieved remarkable success in
generating fluent and coherent text. However, these models often tend to'hallucinate'which …