Survey of Hallucination in Natural Language Generation

https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=llm+... https://www.medpagetoday.com/ophthalmology/generalophthalmol.... - Survey of Hallucination in Natural Language Generation](https://arxiv.org/abs/2202.03629) (Ji et al., 2022) - [How Language Model Hallucinations Can Snowball](https://arxiv.org/abs/2305.13534) (Zhang et al., 2023) - [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://arxiv.org/abs/2302.04023) (Bang et al., 2023) - [Contrastive Learning Reduces Hallucination in Conversations](https://arxiv.org/abs/2212.10400) (Sun et al., 2022) - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) (Wang et al., 2022) - [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://arxiv.org/abs/2303.08896) ( Manakul et al., 2023)

Although some people insist (as you do) that "hallucination" is unreasonably anthropomorphic language, it is an extremely common term of art in the field. eg https://dl.acm.org/doi/abs/10.1145/3571730 Secondly, to be anthropormorphic, hallucination would have to be exclusively human, and why should hallucination be a purely human phenomenon? Consider this Stanford study on lab mice https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6711485/ . The purpose of the study is described as being to understand hallucination and it is described by the scientists involved informally as involving hallucinating mice eg here https://www.sciencedaily.com/releases/2019/07/190718145358.h... . It does involve inducing mice to see things which are not there and behave accordingly. Most people would call that a hallucination.

Here you go, here's a bunch of papers that you have not read. If you had read them then you would know that the errors are predictable and therefore there are many measurable ways to make improvements. Toolformer: Language Models Can Teach Themselves to Use Tools: https://arxiv.org/abs/2302.04761 PAL: Program-aided Language Models: https://arxiv.org/abs/2211.10435 TALM: Tool Augmented Language Models: https://arxiv.org/abs/2205.12255 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models: https://arxiv.org/abs/2201.11903 Survey of Hallucination in Natural Language Generation: https://arxiv.org/abs/2202.03629

It's a technical term: Survey of Hallucination in Natural Language Generation (2022): https://arxiv.org/abs/2202.03629 From source: "Once again, intrinsic hallucination refers to output content that contradicts the source, while extrinsic hallucination refers to output content that the source cannot verify."

If you're looking for academic research, this is a great place to start: https://arxiv.org/abs/2202.03629 But you can get a general feel by using ChatGPT. Open up a new conversation and ask it something like, "What is the capital of France?". Note the response. Open up a new conversation and note the response. Soon enough you should be able to see that the responses are far from random. You can use the OpenAI APIs directly and have it run 10,000 or so iterations to see what kind of "hallucinations" it makes! They are not random!