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@casper_hansen_

One of the core limitations of language models: they struggle with true novelty. LLMs remix known information. If something isn’t in the pretraining or finetuning data, they have very little to work with. This is why there are a million papers chasing +5 points on GSM8k. https://t.co/9LFX80iiyR

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                              "full_text": "@casper_hansen_ Isn’t that’s where Fine tuning , search enabled tools comes to play and carefully drafted RAG with multiple few shot + 1COT prompts helps . Thoughts",
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                              "full_text": "@casper_hansen_ obviously it cant deal with novel things, but have you found them effective in atleast helping you find the source that would help you solve that problem?\nig this is much easier to do for an llm but llms still lack a ton here",
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                              "full_text": "@casper_hansen_ @jeremyphoward What’s special or different about how humans think that allows us come up with truly novel ideas? I suppose the fact that we can continuously learn and “update our weights” on the fly so to speak makes a huge difference?",
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                              "full_text": "@casper_hansen_ What's the prompt? I want to test it with prompt engineering and an internal model. You can send in DM if you don't want that question to be contaminated in any web scrapper dataset",
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                              "full_text": "@casper_hansen_ @jeremyphoward The current training objective for LLMs is the most probable next token. If we want novel outputs, we need to change it so. The hard part is training it for novel outputs. How does that dataset look like? Is it still the transformer architecture? Was @Yanlecunn correct all along?",
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