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2025-09-10 06:49:03
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Robert “The Bobby Yaga” McNees
via
Dr. Victoria Grinberg
mcnees@mastodon.social
For everyone who asked, here’s the full section from the syllabus.
Should I Use ChatGPT Or Another LLM To Study? I wouldn't recommend it. I try to keep up with the capabilities of the major LLMs. They can do some things really well, if you use them the right way. However, they frequently make mistakes when generating responses to questions about physics. Sometimes these mistakes are obvious, sometimes they are subtle and hard to spot. The fact that you cannot trust the output of LLMs should be reason enough not to rely on these systems when you are trying to learn a new subject. But that's not the only problem. Interactions with LLMs feel like a dialog, so it's natural to think the usual rules of conversation apply. You ask a question and expect the response will be an answer to that question. It's important to understand that this is not what's happening. An LLM is designed to generate statistically likely responses to the question "What would an answer to this query sound like?" This is not the same thing as answering the question. It might produce what you are looking for, or it might not. This is one reason why output from an LLM will sound authoritative even when it's wrong, and apologetic when mistakes are pointed out. It isn't authoritative or apologetic, and it isn't "thinking" about your question. These are just the sorts of responses that best fit a very complicated set of likelihood criteria…
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![Should I Use ChatGPT Or Another LLM To Study? I wouldn't recommend it. I try to keep up with the capabilities of the major LLMs. They can do some things really well, if you use them the right way. However, they frequently make mistakes when generating <br />responses to questions about physics. Sometimes these mistakes are obvious, sometimes they are subtle and hard to spot. The fact that you cannot trust the output of LLMs should be reason enough not to rely on these systems when you are trying to learn a <br />new subject. But that's not the only problem. Interactions with LLMs feel like a dialog, so it's natural to think the usual rules of conversation apply. You ask a question and expect the response will be an answer to that question. It's important to <br />understand that this is not what's happening. An LLM is designed to generate statistically likely responses to the question "What would an answer to this query sound like?" This is not the same thing as answering the question. It might produce <br />what you are looking for, or it might not. This is one reason why output from an LLM will sound authoritative even when it's wrong, and apologetic when mistakes are pointed out. It isn't authoritative or apologetic, and it isn't "thinking" about <br />your question. These are just the sorts of responses that best fit a very complicated set of likelihood criteria…](https://files.mastodon.social/media_attachments/files/115/178/192/172/684/368/original/7addc596b85ad414.jpeg "Should I Use ChatGPT Or Another LLM To Study? I wouldn't recommend it. I try to keep up with the capabilities of the major LLMs. They can do some things really well, if you use them the right way. However, they frequently make mistakes when generating <br />responses to questions about physics. Sometimes these mistakes are obvious, sometimes they are subtle and hard to spot. The fact that you cannot trust the output of LLMs should be reason enough not to rely on these systems when you are trying to learn a <br />new subject. But that's not the only problem. Interactions with LLMs feel like a dialog, so it's natural to think the usual rules of conversation apply. You ask a question and expect the response will be an answer to that question. It's important to <br />understand that this is not what's happening. An LLM is designed to generate statistically likely responses to the question "What would an answer to this query sound like?" This is not the same thing as answering the question. It might produce <br />what you are looking for, or it might not. This is one reason why output from an LLM will sound authoritative even when it's wrong, and apologetic when mistakes are pointed out. It isn't authoritative or apologetic, and it isn't "thinking" about <br />your question. These are just the sorts of responses that best fit a very complicated set of likelihood criteria…")
