This is great! Addressing the model limitations a bit: in the demonstration data that is provided to the base model, we should prevent computed or "looked up"

This is great! Addressing the model limitations a bit: in the demonstration data that is provided to the base model, we should prevent computed or "looked up" answers. I've seen some of the demonstration data that people are using to train instruction-tuned models and are being taught to respond by making up answers to solutions it shouldn't try to compute. Btw, the output is wrong. { "instruction": "What would be the output of the following JavaScript snippet?", "input": "let area = 6 * 5;\nlet radius = area / 3.14;", "output": "The output of the JavaScript snippet is the radius, which is 1.91." }, [1] The UI note for now would get us very far but by filtering out demonstrations that retrieve or compute information we can go even farther. Symbol tuning [2] is addressing the quality of demonstrations but we can take it further by removing retrievals and computations altogether. Bonus: we can demonstrate how to make it respond so that the user/agent be informed of how to compute or retrieve. 1: https://github.com/sahil280114/codealpaca/commit/0d265112c705f88848783dfdf9bdca9df64d8e35#diff-0585315a2292ca9c3186c41e52ce9427a39b5f595d4bee6aae15e9443c95051b https://github.com/sahil280114/codealpaca/commit/0d265112c70... 2: https://arxiv.org/abs/2305.08298 https://arxiv.org/abs/2305.08298