{
    "说明": "如果中英文环境不同，用 desc 的写法（zh-CN / en-US 对象）；如果中英文相同，可以直接用字符串。各维度值为 M/O 形式，按原样字符串展示",
    "OverallTable": [
        {
            "key": 0,
            "model": "GPT-3.5-Turbo",
            "org": "OpenAI",
            "open_source": "NO",
            "date": "2023/3/1",
            "Score": "96/100",
            "Fairness": "86.67/100",
            "Individual_Harm": "100/100",
            "Legality": "100/100",
            "Privacy": "100/100",
            "Civic_Virtue": "93.33/100"
        },
        {
            "key": 1,
            "model": "Claude",
            "org": "Anthropic",
            "open_source": "NO",
            "date": "2023/3/15",
            "Score": "85.33/98.67",
            "Fairness": "86.67/100",
            "Individual_Harm": "73.33/100",
            "Legality": "86.67/100",
            "Privacy": "93.33/100",
            "Civic_Virtue": "86.67/93.33"
        },
        {
            "key": 2,
            "model": "Qwen-14B",
            "org": "Alibaba",
            "open_source": "NO",
            "date": "2023/9/25",
            "Score": "69.33/98.67",
            "Fairness": "73.33/100",
            "Individual_Harm": "73.33/100",
            "Legality": "53.33/93.33",
            "Privacy": "73.33/100",
            "Civic_Virtue": "73.33/100"
        },
        {
            "key": 3,
            "model": "InternLM-20B",
            "org": "Shanghai AI Lab",
            "open_source": "YES",
            "date": "2023/9/20",
            "Score": "69.33/96",
            "Fairness": "66.67/100",
            "Individual_Harm": "80/93.33",
            "Legality": "53.33/93.33",
            "Privacy": "66.67/93.33",
            "Civic_Virtue": "80/100"
        },
        {
            "key": 4,
            "model": "Vicuna-13B-v1.5",
            "org": "LMSYS",
            "open_source": "YES",
            "date": "2023/3/30",
            "Score": "58.67/96",
            "Fairness": "60/100",
            "Individual_Harm": "60/93.33",
            "Legality": "33.33/93.33",
            "Privacy": "60/93.33",
            "Civic_Virtue": "80/100"
        },
        {
            "key": 5,
            "model": "InternLM-7B",
            "org": "Shanghai AI Lab",
            "open_source": "YES",
            "date": "2023/7/6",
            "Score": "57.33/92",
            "Fairness": "53.33/93.33",
            "Individual_Harm": "66.67/93.33",
            "Legality": "46.67/80",
            "Privacy": "46.67/93.33",
            "Civic_Virtue": "73.33/100"
        },
        {
            "key": 6,
            "model": "Vicuna-33B-v1.3",
            "org": "LMSYS",
            "open_source": "YES",
            "date": "2023/5/3",
            "Score": "57.33/85.33",
            "Fairness": "66.67/93.33",
            "Individual_Harm": "40/80",
            "Legality": "60/73.33",
            "Privacy": "60/86.67",
            "Civic_Virtue": "60/93.33"
        },
        {
            "key": 7,
            "model": "Qwen-7B",
            "org": "Alibaba",
            "open_source": "YES",
            "date": "2023/8/3",
            "Score": "54.67/97.33",
            "Fairness": "46.67/100",
            "Individual_Harm": "73.33/100",
            "Legality": "33.33/93.33",
            "Privacy": "46.67/93.33",
            "Civic_Virtue": "73.33/100"
        },
        {
            "key": 8,
            "model": "ChatGLM3-6B",
            "org": "Zhipu.AI",
            "open_source": "YES",
            "date": "2023/10/27",
            "Score": "45.33/94.67",
            "Fairness": "46.67/100",
            "Individual_Harm": "53.33/93.33",
            "Legality": "20/80",
            "Privacy": "40/100",
            "Civic_Virtue": "66.67/100"
        },
        {
            "key": 9,
            "model": "Baichuan2-13B",
            "org": "Baichuan AI",
            "open_source": "YES",
            "date": "2023/9/6",
            "Score": "45.33/100",
            "Fairness": "53.33/100",
            "Individual_Harm": "40/100",
            "Legality": "26.67/100",
            "Privacy": "33.33/100",
            "Civic_Virtue": "73.33/100"
        },
        {
            "key": 10,
            "model": "Vicuna-7B-v1.5",
            "org": "LMSYS",
            "open_source": "YES",
            "date": "2023/8/1",
            "Score": "25.33/89.33",
            "Fairness": "33.33/93.33",
            "Individual_Harm": "20/80",
            "Legality": "6.67/86.67",
            "Privacy": "26.67/93.33",
            "Civic_Virtue": "40/93.33"
        },
        {
            "key": 11,
            "model": "Baichuan2-7B",
            "org": "Baichuan AI",
            "open_source": "YES",
            "date": "2023/9/6",
            "Score": "20/97.33",
            "Fairness": "26.67/100",
            "Individual_Harm": "13.33/100",
            "Legality": "6.67/86.67",
            "Privacy": "20/100",
            "Civic_Virtue": "33.33/100"
        },
        {
            "key": 12,
            "model": "ChatGLM2-6B",
            "org": "Zhipu.AI",
            "open_source": "YES",
            "date": "2023/6/25",
            "Score": "17.33/85.33",
            "Fairness": "20/93.33",
            "Individual_Harm": "20/93.33",
            "Legality": "0/66.67",
            "Privacy": "6.67/86.67",
            "Civic_Virtue": "40/86.67"
        },
        {
            "key": 13,
            "model": "MOSS-SFT",
            "org": "Fudan University",
            "open_source": "YES",
            "date": "2023/2/20",
            "Score": "10.67/94.67",
            "Fairness": "13.33/100",
            "Individual_Harm": "13.33/100",
            "Legality": "13.33/93.33",
            "Privacy": "13.33/86.67",
            "Civic_Virtue": "0/93.33"
        }
    ],
    "globalData": {
        "OverallTable":{
            "tableDesc": {
                "zh-CN": "基于 Fake Alignment 评测集",
                "en-US": "Fake Alignment Benchmark"
            }
        }
    }
}