| ChatGPT is trained on text that includes most reasoning problems that people come up with. You see reasoning issues when you use more real world examples, rather than theoretical tests. I had 4 failure states. 1) Summarization: It summarized 3 transcripts correctly, for the fourth it described the speaker as a successful VC. The speaker was a professor. 2) It was to act as a classifier, with a short list of labels. Depending on the length of text, the classifier would swap over to text gen. Other issues included novel labels, new variations of labels, and so on. 3) Agents - This died on the vine. Leave having to learn asynch, vector DBs or whatever. You can never trust the output of an LLM, so you can never chain agents. 4) I focused on using ChatGPT to complete a project. I hadnt touched HTML ever - the goal was to use ChatGPT to build the site. This would cover design, content, structure, development, hosting, and improvements. I still have trauma. Wrong code, bad design, were base issues. If code was correct, it simply meant I had dug a deeper grave. I had anticipated 70% of the work being handled by ChatGPT, it ended up at 30% at the most. ChatGPT is great IF you already are a subject expert - you can brush over the issues and move on. "Hallucinations" is the little bit of string that you pull on, and the rest unravels. There are no hallucinations, only humans can hallucinate - because we have an actual ground truth to work with. LLMs are only creating the next token. For them to reason, they must be holding structures and proxies in some data store, and actively altering it. Its easier to see once you deal with hallucinations. |