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Here you go, here's a bunch of papers that you have not read. If you had read them then you would know that the errors are predictable and therefore there are m

Here you go, here's a bunch of papers that you have not read. If you had read them then you would know that the errors are predictable and therefore there are many measurable ways to make improvements. Toolformer: Language Models Can Teach Themselves to Use Tools: https://arxiv.org/abs/2302.04761 https://arxiv.org/abs/2302.04761 PAL: Program-aided Language Models: https://arxiv.org/abs/2211.10435 https://arxiv.org/abs/2211.10435 TALM: Tool Augmented Language Models: https://arxiv.org/abs/2205.12255 https://arxiv.org/abs/2205.12255 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models: https://arxiv.org/abs/2201.11903 https://arxiv.org/abs/2201.11903 Survey of Hallucination in Natural Language Generation: https://arxiv.org/abs/2202.03629 https://arxiv.org/abs/2202.03629

sarchertech 4y ago
I took the time to read through the only one of those that looked like it was peer reviewed and read the abstracts for the rest. Survey of Hallucination in Natural Language Generation only provided promising methods for detecting hallucinations in summarization tasks, which are of course much easier to detect. Searching arXiv for a list of non-reviewed papers that sound like they might be related to the topic at hand is fun debate strategy. But no one else is reading this far into an old thread, so I'm not sure who you're trying to convince. None of these paper prove your claims about hallucinations, and most aren't even trying to. However, even if the errors that I'm saying aren't meaningfully probabilistic aren't hallucinations.