{"query": "Could you elucidate on the values of temperature and top-p that are utilized for pass@1 scores?", "pos": "8 62.8\n13B 18.3 60.2 30.6 69.0\n34B 22.6 77.2 33.0 76.1\n70B29.9 89.0 45.0 81.4\nTable 21: Code generation results on Human-Eval and MBPP . We report 0-shot and 3-shot results for\nHuman-Eval and MBPP respectively. For pass@100 and pass@80 scores, we use a temperature of 0.8 and\ntop-p=0.95. For pass@1 scores, we use a temperature of 0.1 and top- p=0.95.\n49"}
{"query": "What do high safety scores and low helpfulness ratings suggest?", "pos": "Here we show more evidence and\nqualitative results to manifest this tension. Figure32 are two scatter plots of helpfulness and safety reward\nmodel scores on the safety test set for safe and unsafe responses. The tension can be observed at the bottom\nright corner (i.e., high safety score but low helpfulness score) in the safe response plot (left) and the top left\ncorner (i.e., low safety score but high helpfulness score) in the unsafe response plot (right). We also list two\nqualitative examples where safety and helpfulness reward models don’t agree with each other in Table 35."}
{"query": "The process of carefully adjusting precautions relies on using challenging stimuli together with protected displays to make its operation run more smoothly.", "pos": "4.2 Safety Fine-Tuning\nIn this section, we describe our approach to safety fine-tuning, including safety categories, annotation\nguidelines,and the techniques we use to mitigate safety risks. We employ a process similar to the general\nfine-tuning methods as described in Section 3, with some notable differences related to safety concerns.\nSpecifically, we use the following techniques in safety fine-tuning:\n1.Supervised Safety Fine-Tuning : We initialize by gathering adversarial prompts and safe demonstra-\ntions that are then included in the general supervised fine-tuning process (Section 3.1). This teaches\nthe model to align with our safety guidelines even before RLHF,and thus lays the foundation for\nhigh-quality human preference data annotation."}
