🤖 Generative AI Lead @ AWS ☁️ (150k+) | Startup Advisor | Public Speaker | AI Outsider | Founder Thinkfluencer AI
🤯 𝗟𝗟𝗠𝘀 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀: 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗼𝗿 𝗕𝘂𝗴? Hallucinations in Large Language Models (LLMs) occur when the model generates plausible-sounding but inaccurate or nonsensical responses. This is a major reason why many customers hesitate to push back their prototypes into production, especially for end-customer-facing applications. 𝑾𝒉𝒚 𝑫𝒐 𝑯𝒂𝒍𝒍𝒖𝒄𝒊𝒏𝒂𝒕𝒊𝒐𝒏𝒔 𝑯𝒂𝒑𝒑𝒆𝒏? 🔸𝟭. 𝗟𝗶𝗺𝗶𝘁𝗲𝗱 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴: LLMs transform prompts and training data into abstractions, leading to potential loss of information. 🔸𝟮. 𝗡𝗼𝗶𝘀𝗲 𝗼𝗿 𝗕𝗶𝗮𝘀𝗲𝘀 𝗶𝗻 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗗𝗮𝘁𝗮: Skewed statistical patterns can cause unexpected responses. 🔸𝟯. 𝗢𝘃𝗲𝗿𝗳𝗶𝘁𝘁𝗶𝗻𝗴: High model complexity, combined with incomplete or biased training data, often leads to hallucinations. Some researchers argue that hallucinations are inevitable due to LLMs' inherent limitations. Others view them as a feature, fostering creativity. From my perspective, both views are valid. Hallucinations can indeed result in catastrophic errors—like Chevy recommending a Ford or Google suggesting you eat rocks. However, much like in real life, these hallucinations can also spark creativity, allowing LLMs to generate innovative ideas. Balancing these aspects is key to leveraging the full potential of LLMs while mitigating risks. #AI #MachineLearning #LLM #Innovation #TechTrends
If you know what you are doing, *maybe* feature. If you dont, always drama.
LLM is like a friend who is always high.
Bug for everything outside of where creativity is a need. Bug even in those instances because it shows a fundamental lack of understanding of what it is saying which isn't all that intelligent (though some humans would follow suit).
Unexpected responses is a nice way of saying incorrect responses. Let's not use soft language here, this is science not your kindergarten.
Spurious correlations is a better description of this phenomenon. Whether they are creative or hallucinatory, that's a human opinion.
Thank you for sharing Eduardo! The explanation of LLM hallucinations is insightful. Addressing these issues by improving contextual understanding and data quality is crucial. Balancing creativity and accuracy will be essential for future advancements in this field.
To mitigate LLM hallucinations, consider enhancing contextual understanding, reducing noise and biases in training data, and preventing overfitting. Additionally, integrating knowledge graphs and employing visual contrastive decoding are promising strategies.
Oh man. That's funny. Well done.
Eduardo Ordax just tell the LLM researches to embed the impressions with search embeddings😎. It’s akin to Channel surfing between #bbc, #cnn, #fox… it’s how DocNote.ai unlocks medical jargon where hallucinations 🚫 as you know. Cheers!
LLMs hallucinate due to limited contextual understanding, noise or biases in training data, and overfitting. These factors lead to the generation of plausible-sounding but inaccurate or nonsensical responses. Hallucinations in Language Models (LLMs) can significantly impact their performance by generating plausible-sounding but inaccurate or nonsensical responses. This issue causes hesitation in deploying LLMs for customer-facing applications due to the potential for catastrophic errors, such as providing completely wrong information. However, hallucinations can also foster creativity by generating innovative ideas, suggesting a balance between leveraging their potential and mitigating risks is essential.