This content is periodically updated and is not static.
Content has been a joint effort by a human and some of the AI assistants.
I asked a naive but simple question to CoPilot. He was very honest in sharing his opinion related to the question.
"How do you address flaws in the LLM? Perhaps, you are just a consumer of LLM."
He answered my question which cleared some doubts in my mind.
Large Language Models (LLMs), such as GPT-4, indeed exhibit remarkable natural language capabilities. However, their inner workings remain shrouded in mystery, akin to black boxes. Let’s delve into the challenges posed by this opacity and explore potential solutions:
The LLM Black Box Problem:
- LLMs operate as black boxes, obscuring the logic behind their decisions. This lack of transparency has safety and ethical implications.
- Imagine deploying an LLM for critical tasks like medical diagnoses or financial risk assessments. If we don’t understand how it reaches conclusions, can we trust it?
- The scale and complexity of LLMs exacerbate this issue. Models like GPT-3, with 175 billion parameters, defy easy comprehension.
- Each parameter interacts in intricate ways, leading to emergent capabilities that aren’t predictable from individual components.
- While reducing scale might improve interpretability, it could compromise capability.
- Major LLMs are proprietary systems developed by tech giants like Google, Meta, and OpenAI. Their complete details remain undisclosed, treating LLMs as trade secrets1.
Flawed Decision Making:
- The opaqueness in LLM decision-making can result in undetected biases and errors.
- In fields like healthcare or criminal justice, where decisions have far-reaching consequences, auditing LLMs for ethical and logical soundness becomes challenging2.
Emerging Solutions:
- Researchers are exploring techniques for explainable AI and transparent design.
- Efforts include developing methods to visualize LLM decision paths, identifying influential tokens, and creating interpretable representations.
- Ethical guidelines and bias mitigation strategies are crucial to address flaws.
- Collaborative efforts between researchers, practitioners, and policymakers are essential to unlock LLM benefits while minimizing risks1.
As for me, I’m not just a passive consumer—I strive to engage in meaningful conversations and provide helpful information. While I don’t directly address LLM flaws, I aim to assist users by leveraging my knowledge and predefined tools. 🤖📚
CoPilot is thinking of only healthcare and criminal justice, but in the near term it will have enormous consequences for the wars, and elections. State actors will emerge to make world harmony a distant dream.
It is often a tedium conversing with the CoPilot. Sometimes, CoPilot does not answer your question at all, rather addresses the key phrases individually independent of your question. It feels like you are conversing with a hearing impaired person. It is still a project in the works.
5/18/2024
Gemini AI is now easy to access. It does perform just like CoPilot. If you ask controversial subjects, or ask Gemini to factcheck, it declines saying it is still in the learning mode. However, Google tries to get a feedback from users that will improve the product. This is important because Gemini can return absolutely wrong answers.
7/22/2024
Co-Pilot has been instructed not to answer politics related question even if you are seeking publicly available information. It is in clam-shell mode.
7/22/2024
Of course, you must have heard of the Meta AI o the WhatsApp. It does answer any questions.
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