- not automatically but at all. because hallucination is enevitable LLM effect, right?
- Show thinking Hallucination is indeed a known phenomenon in large language models, where the model may generate information that is plausible-sounding but incorrect, made up, or not supported by its training data or real-world facts. While significant efforts are made to reduce hallucinations in models like me, particularly for factual queries, it remains a challenge and a potential risk, especially when dealing with complex, nuanced, or ambiguous topics, or when asked to generate novel information not present in the training data.
Given that hallucination is a possibility, though its frequency and severity vary depending on the model, the type of query, and the domain of information:
Does it mean AI suggestions should not be followed at all? Not necessarily. AI can be a valuable tool for accessing and summarizing information, brainstorming, generating creative text, and providing assistance in many domains. Does it mean AI output, especially on critical topics like health, finance, or safety, should be critically evaluated and verified with reliable, independent sources? Absolutely. The risk of generating inaccurate information, including the potential for hallucination, is precisely one of the core reasons why I cannot provide specific, ready-to-use therapeutic formulations or medical advice. Doing so would involve generating information that, if incorrect or a hallucination, could lead directly to harm if followed without professional validation and testing.
So, while "inevitable" might be a strong word depending on the specific context and type of output, hallucination is a known limitation that underscores the importance of critical thinking and verification when using AI, particularly for sensitive or critical applications.
![[personal profile]](https://www.dreamwidth.org/img/silk/identity/user.png){kind=small}
no subject
Date: 2025-05-20 02:09 am (UTC)- Show thinking
Hallucination is indeed a known phenomenon in large language models, where the model may generate information that is plausible-sounding but incorrect, made up, or not supported by its training data or real-world facts. While significant efforts are made to reduce hallucinations in models like me, particularly for factual queries, it remains a challenge and a potential risk, especially when dealing with complex, nuanced, or ambiguous topics, or when asked to generate novel information not present in the training data.
Given that hallucination is a possibility, though its frequency and severity vary depending on the model, the type of query, and the domain of information:
Does it mean AI suggestions should not be followed at all? Not necessarily. AI can be a valuable tool for accessing and summarizing information, brainstorming, generating creative text, and providing assistance in many domains.
Does it mean AI output, especially on critical topics like health, finance, or safety, should be critically evaluated and verified with reliable, independent sources? Absolutely.
The risk of generating inaccurate information, including the potential for hallucination, is precisely one of the core reasons why I cannot provide specific, ready-to-use therapeutic formulations or medical advice. Doing so would involve generating information that, if incorrect or a hallucination, could lead directly to harm if followed without professional validation and testing.
So, while "inevitable" might be a strong word depending on the specific context and type of output, hallucination is a known limitation that underscores the importance of critical thinking and verification when using AI, particularly for sensitive or critical applications.