Oct 29, 03:03 AM

Researchers Highlight Inherent Hallucinations in Large Language Models Due to Undecidable Problems and Ongoing Speed, Cost, Reliability Challenges

Recent discussions among AI researchers highlight the persistent issue of hallucinations in large language models (LLMs), which are considered an inherent property rather than mere errors. A paper suggests that these hallucinations stem from undecidable problems encountered during the training and usage of LLMs. Despite ongoing advancements in LLM technology, challenges such as speed, cost, and reliability continue to hinder their application in real business scenarios. Experts emphasize that simply utilizing faster or larger GPUs will not resolve these fundamental issues, and a collective understanding within the AI community is necessary for addressing the limitations of LLMs.

Written with ChatGPT (GPT-4o mini).

Sources

deter3@deter3
2 years ago
LLM models' hallucination comes from AI community's collective illusion. No matter how much large language models improve, speed, cost, and reliability remain major barriers for real business applications. Simply using faster or larger GPUs isn't the solution, and neither is…
deter3@deter3
2 years ago
LLM models' hallucination comes from AI community's collective illusion . No matter how much large language models improve, speed, cost, and reliability remain major barriers for real business applications. Simply using faster or larger GPUs isn't the solution, and neither is…
Towards Data Science@TDataScience
2 years ago
🤖 As researchers tackle the limitations of LLMs, the potential for developing models with human-like reasoning capabilities is within reach. Read more in Alexander Watson's latest article. #LLM #MachineLearning https://t.co/11ALpmbF54

Researchers Highlight Inherent Hallucinations in Large Language Models Due to Undecidable Problems and Ongoing Speed, Cost, Reliability Challenges

Sources

Additional media