<My Opinion>
LLM Hallucination. It must defenitely be a serious technical problem when you are using LLMs(Large Language Models).
LLM hallucinations mean "the events in which ML models, particularly large language models (LLMs) like GPT-3 or GPT-4, produce outputs that are coherent and grammatically correct but factually incorrect or nonsensical."
As LLM-related technology has been evolving, LLM users are getting confused because of misinformation caused by LLM hallucinations. LLMs scrape training data from everything without verifying whether the data is real or not.
This matter can jeopardize LLM users to have misunderstandings. Eventually, this matter could lead to a social chaos.
Therefore, a norm for training LLMs in proper way must be established to prevent harmful consequences caused by LLMs. In that sense, BigScience Project is important project that can raise awareness and carefulness among LLM developers. The LLM develops should resposibly train LLMs so that results spewed out of LLMs do not lead users to misunderstand about the matter they wondered about. Also, LLMs should provide multilingual support so that people around the world can use them equally. Consequently, users outside of major language communities must be given the opportunity to access information on par with users in major language environments.
<ChatGPT 첨삭>
LLM Hallucination: A Serious Technical Challenge in Large Language Models
LLM hallucinations refer to instances where machine learning models, particularly large language models (LLMs) such as GPT-3 or GPT-4, generate responses that are grammatically correct and coherent but factually inaccurate or nonsensical.
As LLM technology continues to evolve, misinformation caused by hallucinations has led to growing confusion among users. This issue arises because LLMs are trained on vast amounts of data without an inherent ability to verify its accuracy. Such misinformation can mislead users and contribute to widespread misunderstandings. In extreme cases, this could even lead to social disruption.
To mitigate these risks, standardized guidelines for training LLMs properly must be established to prevent harmful consequences. In this regard, initiatives like the BigScience Project play a crucial role in raising awareness and encouraging responsible AI development. LLM developers must train these models with accountability, ensuring that the outputs they generate do not mislead users. Additionally, LLMs should offer robust multilingual support to promote equitable access to information worldwide. People outside major language communities must have the same opportunities to access reliable information as those in dominant linguistic environments.
<평가>
끝!
