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a\u6717\u4e5f\u662f\u4e00\u6837\u7684\u9053\u7406\uff0c\u901a\u8fc7\u519b\u4e8b\u884c\u52a8\u65e2\u95f4\u63a5\u6572\u6253\u4e86\u4ee5\u8272\u5217\u548c\u80cc\u540e\u7ed9\u4e88\u652f\u6301\u7684\u7f8e\u65b9\uff0c\u800c\u4e14\u4e5f\u52a0\u5f3a\u4e86\u4e0e\u4f0a\u6717\u548c\u53d9\u5229\u4e9a\u4e4b\u95f4\u7684\u540c\u76df\u5173\u7cfb\uff0c\u4fc4\u65b9\u4f55\u4e50\u800c\u4e0d\u4e3a\u5462\uff1f\n\n\u5728\u8fd9\u573a\u6fc0\u6218\u4e2d\uff0c\u8c01\u90fd\u4e0d\u670d\u8c01\uff0c\u76f8\u4fe1\u63a5\u4e0b\u6765\u5c31\u7b97\u7f8e\u56fd\u4f9d\u65e7\u503e\u5927\u529b\u5bf9\u4ee5\u8272\u5217\u8fdb\u884c\u76f8\u52a9\uff0c\u90a3\u4e48\u4fc4\u7f57\u65af\u4e5f\u5fc5\u4e0d\u4f1a\u4efb\u5176\u80e1\u4f5c\u975e\u4e3a\u3002\u81f3\u4e8e\u6218\u4e8b\u7a76\u7adf\u4f1a\u8d70\u5411\u600e\u6837\u7684\u5c40\u9762\uff0c\u4e5f\u53ea\u80fd\u4ea4\u7ed9\u65f6\u95f4\u6765\u56de\u7b54\u4e86\u3002\n\n**\u672a\u6765\u7684\u5c40\u52bf\u5c06\u4f1a\u662f\u8c01\u90fd\u65e0\u6cd5\u9884\u6d4b\u7684\uff1f**\n\n\u4f60\u65b9\u5531\u7f62\u6211\u767b\u573a\uff0c\u5df4\u4ee5\u51b2\u7a81\u7684\u6301\u7eed\u5347\u7ea7\uff0c\u4e2d\u4e1c\u5c40\u52bf\u8d8a\u6765\u8d8a\u8d70\u5411\u4e0d\u53ef\u63a7\u7684\u5c40\u9762\u3002\u5168\u7403\u98ce\u96e8\u6b32\u6765\uff0c\u8c01\u90fd\u65e0\u6cd5\u9884\u6d4b\u672a\u6765\u7684\u5c40\u52bf\u5c06\u4f1a\u662f\u600e\u6837\u7684\uff1f","num_tokens":1141}],"turn":1,"language":"Chinese","category_tag":{"criteria_v0.1":{"specificity":false,"domain_knowledge":true,"complexity":false,"problem_solving":false,"creativity":true,"technical_accuracy":false,"real_world":true},"if_v0.1":{"if":true,"score":4},"math_v0.1":{"math":false}}}
{"question_id":10467,"category":"Instruction Following","model_a":"mixtral-8x22b-instruct-v0.1","model_b":"gemma-1.1-2b-it","winner":"tie","conversation_a":[{"role":"user","content":"\u7f16\u7a0b\u7ec3\u4e60\u9898,\u7ed9\u6211\u5b8c\u6574\u89e3\u9898\u4ee3\u7801\n\u4ee5Rosenbrock\u51fd\u6570\u6700\u5c0f\u5316\u4e3a\u4f8b\uff0c\u8bbe\u8ba1\u4e00\u79cd\u6a21\u62df\u9000\u706b\u7b97\u6cd5\u3002\uff08\u5b9e\u73b0\u8bed\u8a00\u4e0d\u9650\uff09\n\u591a\u5143\u51fd\u6570\uff08\u7b49\u9ad8\u7ebf\uff0f\u7b49\u76f4\u7ebf\u3001\u68af\u5ea6\u3001Hessian\u9635\uff09\n23f(x)=100(x2-x2)2+(1-x1)216Rosenbrock\"\u9999\u8549\"\u51fd\u6570","num_tokens":97},{"role":"assistant","content":"\u4ee5\u4e0b\u662f\u7528Python\u7f16\u5199\u7684\u6a21\u62df\u9000\u706b\u7b97\u6cd5\uff0c\u7528\u4e8e\u6700\u5c0f\u5316Rosenbrock\u51fd\u6570\u3002\n\n```python\nimport numpy as np\nimport random\nimport matplotlib.pyplot as plt\n\n# Rosenbrock function\ndef rosenbrock(x):\n return 100 * (x[1] - x[0]**2)**2 + (1 - x[0])**2\n\n# Initial solution\nx_init = np.array([-1.2, 1])\n\n# Parameters\nT_initial = 100 # Initial temperature\nT_final = 1e-5 # Final temperature\nalpha = 0.99 # Cooling rate\nnum_iterations = 10000 # Number of iterations\n\ndef simulated_annealing(x_init, T_initial, T_final, alpha, num_iterations):\n T = T_initial\n x_best = x_init\n f_best = rosenbrock(x_best)\n\n x = x_init\n f = rosenbrock(x)\n\n iterations = 0\n while T > T_final and iterations < num_iterations:\n iterations += 1\n\n # Generate new solution\n x_new = np.random.normal(x, 0.1)\n f_new = rosenbrock(x_new)\n\n # Calculate difference in cost\n delta_f = f_new - f\n\n # Acceptance probability\n p = np.exp(-delta_f \/ T)\n\n # Accept or reject new solution\n if delta_f < 0 or random.random() < p:\n x = x_new\n f = f_new\n\n # Update best solution\n if f < f_best:\n x_best = x\n f_best = f\n\n # Cooling\n T = alpha * T\n\n return x_best, f_best\n\n# Run simulated annealing\nx_best, f_best = simulated_annealing(x_init, T_initial, T_final, alpha, num_iterations)\n\nprint(\"Best solution: \", x_best)\nprint(\"Best cost: \", f_best)\n\n# Plot Rosenbrock function and best solution\nx1 = np.linspace(-1.5, 1.5, 100)\nx2 = np.linspace(-0.5, 2.5, 100)\nX1, X2 = np.meshgrid(x1, x2)\nF = rosenbrock(np.array([X1, X2]))\n\nplt.contour(X1, X2, F, 10, cmap=plt.cm.jet)\nplt.plot(x_best[0], x_best[1], 'r*', markersize=10)\nplt.xlabel('$x_1$')\nplt.ylabel('$x_2$')\nplt.title('Rosenbrock function with best solution')\nplt.show()\n```\n\n\u8be5\u4ee3\u7801\u5b9a\u4e49\u4e86Rosenbrock\u51fd\u6570\uff0c\u5e76\u4f7f\u7528\u6a21\u62df\u9000\u706b\u7b97\u6cd5\u8fdb\u884c\u6700\u5c0f\u5316\u3002\u6a21\u62df\u9000\u706b\u7b97\u6cd5\u7684\u53c2\u6570\u5305\u62ec\u521d\u59cb\u6e29\u5ea6\u3001\u6700\u7ec8\u6e29\u5ea6\u3001\u8870\u51cf\u7387\u548c\u8fed\u4ee3\u6b21\u6570\u3002\u5728\u6bcf\u6b21\u8fed\u4ee3\u4e2d\uff0c\u7b97\u6cd5\u751f\u6210\u4e00\u4e2a\u65b0\u7684\u89e3\u51b3\u65b9\u6848\uff0c\u5e76\u6839\u636e\u6210\u672c\u51fd\u6570\u7684\u53d8\u5316\u6765\u51b3\u5b9a\u662f\u5426\u63a5\u53d7\u8be5\u89e3\u51b3\u65b9\u6848\u3002\u5728\u6bcf\u6b21\u8fed\u4ee3\u4e4b\u540e\uff0c\u6e29\u5ea6\u4f1a\u964d\u4f4e\uff0c\u76f4\u5230\u8fbe\u5230\u6700\u7ec8\u6e29\u5ea6\u6216\u8005\u8fbe\u5230\u6240\u9700\u7684\u8fed\u4ee3\u6b21\u6570\u4e3a\u6b62\u3002\u6700\u540e\uff0c\u4ee3\u7801\u4f1a\u5728\u7b49\u9ad8\u7ebf\u56fe\u4e2d\u663e\u793a\u6700\u4f73\u89e3\u51b3\u65b9\u6848\u3002","num_tokens":738}],"conversation_b":[{"role":"user","content":"\u7f16\u7a0b\u7ec3\u4e60\u9898,\u7ed9\u6211\u5b8c\u6574\u89e3\u9898\u4ee3\u7801\n\u4ee5Rosenbrock\u51fd\u6570\u6700\u5c0f\u5316\u4e3a\u4f8b\uff0c\u8bbe\u8ba1\u4e00\u79cd\u6a21\u62df\u9000\u706b\u7b97\u6cd5\u3002\uff08\u5b9e\u73b0\u8bed\u8a00\u4e0d\u9650\uff09\n\u591a\u5143\u51fd\u6570\uff08\u7b49\u9ad8\u7ebf\uff0f\u7b49\u76f4\u7ebf\u3001\u68af\u5ea6\u3001Hessian\u9635\uff09\n23f(x)=100(x2-x2)2+(1-x1)216Rosenbrock\"\u9999\u8549\"\u51fd\u6570","num_tokens":97},{"role":"assistant","content":"**\u6a21\u62df\u9000\u706b\u7b97\u6cd5\u4ee3\u7801**\n\n```python\nimport numpy as np\nfrom scipy.optimize import minimize\n\n# Rosenbrock \u51fd\u6570\ndef rosenbrock(x):\n return 100 * (x[1] - x[0]**2 + (1 - x[0])**2)\n\n# \u6a21\u62df\u9000\u706b\u7b97\u6cd5\ndef simulate_\u9000\u706b(initial_guess, max_iter, temperature):\n for _ in range(max_iter):\n # \u968f\u673a\u9009\u62e9\u4e00\u4e2a\u53c2\u6570\n current_guess = initial_guess + np.random.randn(len(initial_guess)) * np.sqrt(temperature)\n\n # \u8ba1\u7b97\u76ee\u6807\u51fd\u6570\u7684\u503c\n current_value = rosenbrock(current_guess)\n\n # \u6bd4\u8f83\u5f53\u524d\u503c\u548c\u4e0a\u4e00\u4e2a\u503c\n if current_value < current_guess:\n current_guess = current_guess\n print(f\"\u9000\u706b\uff0c\u5f53\u524d\u503c {current_value}\")\n else:\n print(f\"\u6e29\u5ea6 {temperature}, \u5f53\u524d\u503c {current_value}\")\n\n# \u8bbe\u7f6e\u53c2\u6570\ninitial_guess = np.array([0, 0])\nmax_iter = 100\ntemperature = 1.0\n\n# \u8fd0\u884c\u6a21\u62df\u9000\u706b\u7b97\u6cd5\nsimulate_\u9000\u706b(initial_guess, max_iter, temperature)\n```\n\n**\u89e3\u9898\u4ee3\u7801\u89e3\u6790\uff1a**\n\n- **rosenbrock \u51fd\u6570\uff1a**\u5b9a\u4e49\u4e86\u76ee\u6807\u51fd\u6570\uff0c\u8fd9\u662f\u4e00\u4e2a\u7b49\u9ad8\u7ebf\u51fd\u6570\uff0c\u5176\u6700\u5c0f\u503c\u4f4d\u4e8e\u51fd\u6570\u66f2\u7ebf\u7684\u6700\u4f4e\u70b9\u3002\n- **\u6a21\u62df\u9000\u706b\u7b97\u6cd5\uff1a**\u8be5\u7b97\u6cd5\u57fa\u4e8e\u4ee5\u4e0b\u6b65\u9aa4\u6267\u884c\uff1a\n - \u9009\u62e9\u4e00\u4e2a\u521d\u59cb\u731c\u6d4b\u503c\u3002\n - \u5faa\u73af\u5730\u6bd4\u8f83\u5f53\u524d\u503c\u548c\u4e0a\u4e00\u4e2a\u503c\u3002\n - \u5982\u679c\u5f53\u524d\u503c\u6bd4\u4e0a\u4e00\u4e2a\u503c\u5c0f\uff0c\u5219\u66f4\u65b0\u5f53\u524d\u731c\u6d4b\u503c\u3002\n - \u8c03\u6574\u6e29\u5ea6\u503c\uff0c\u4ee5\u51b3\u5b9a\u662f\u5426\u5e94\u8be5\u7ee7\u7eed\u9000\u706b\u3002\n- **\u4ee3\u7801\u4e2d\u7684\u53c2\u6570\uff1a**\n - `initial_guess`\uff1a\u521d\u59cb\u731c\u6d4b\u503c\u3002\n - `max_iter`\uff1a\u9000\u706b\u6700\u5927\u8fed\u4ee3\u6b21\u6570\u3002\n - `temperature`\uff1a\u9000\u706b\u6e29\u5ea6\uff0c\u63a7\u5236\u7740\u591a\u5c11\u65f6\u5019\u8fdb\u884c\u66f4\u65b0\u3002\n- **\u7ed3\u679c\uff1a**\u4ee3\u7801\u5c06\u8f93\u51fa\u6700\u7ec8\u7684\u6700\u5c0f\u503c\u548c\u6536\u655b\u8fc7\u7a0b\u7684\u8be6\u7ec6\u4fe1\u606f\u3002","num_tokens":502}],"turn":1,"language":"Chinese","category_tag":{"criteria_v0.1":{"specificity":true,"domain_knowledge":true,"complexity":true,"problem_solving":true,"creativity":false,"technical_accuracy":true,"real_world":true},"if_v0.1":{"if":true,"score":4},"math_v0.1":{"math":true}}}
{"question_id":16793,"category":"Instruction Following","model_a":"gpt-3.5-turbo-0125","model_b":"im-a-good-gpt2-chatbot","winner":"model_b","conversation_a":[{"role":"user","content":"Slater-Koster\u7684\u7d27\u675f\u7f1a\u6a21\u578b\u81ea\u4ece1954\u5e74\u63d0\u51fa\u540e\u6709\u6ca1\u6709\u5176\u4ed6\u7684\u6539\u8fdb\uff0c\u4f8b\u5982\u5bf9\u4e8e\u4e00\u4e9b\u7f3a\u5931\u77e9\u9635\u5143\u7684\u8865\u9f50\uff0c\u8bf7\u7ed9\u51fa\u6587\u732e\u51fa\u5904","num_tokens":56},{"role":"assistant","content":"\u5173\u4e8eSlater-Koster\u7d27\u675f\u7f1a\u6a21\u578b\u7684\u6539\u8fdb\u548c\u8865\u5145\uff0c\u6709\u5f88\u591a\u6587\u732e\u8fdb\u884c\u4e86\u63a2\u8ba8\u3002\u5176\u4e2d\u4e00\u4e9b\u5305\u62ec\uff1a\n\n1. S. P. Lewis and D. J. Fatseas, \"A revised set of Slater\u2013Koster parameters for the extended Huckel tight-binding model,\" Journal of Physics: Condensed Matter, 3(49), 8989 (1991).\n\n2. D. R. Hamann, M. Schl\u00fcter, and C. Chiang, \"Norm-conserving pseudopotentials,\" Physical Review Letters, 43(20), 1494 (1979).\n\n3. J. M. Soler, E. Artacho, J. D. Gale, A. Garc\u00eda, J. Junquera, P. Ordej\u00f3n, and D. S\u00e1nchez-Portal, \"The SIESTA method for ab initio order-N materials simulation,\" Journal of Physics: Condensed Matter, 14(11), 2745 (2002).\n\n\u8fd9\u4e9b\u6587\u732e\u63d0\u4f9b\u4e86\u4e00\u4e9b\u5173\u4e8eSlater-Koster\u6a21\u578b\u7684\u6539\u8fdb\u548c\u8865\u5145\u7684\u4fe1\u606f\uff0c\u4f60\u53ef\u4ee5\u67e5\u9605\u8fd9\u4e9b\u6587\u732e\u4ee5\u83b7\u53d6\u66f4\u8be6\u7ec6\u7684\u8d44\u6599\u3002","num_tokens":275}],"conversation_b":[{"role":"user","content":"Slater-Koster\u7684\u7d27\u675f\u7f1a\u6a21\u578b\u81ea\u4ece1954\u5e74\u63d0\u51fa\u540e\u6709\u6ca1\u6709\u5176\u4ed6\u7684\u6539\u8fdb\uff0c\u4f8b\u5982\u5bf9\u4e8e\u4e00\u4e9b\u7f3a\u5931\u77e9\u9635\u5143\u7684\u8865\u9f50\uff0c\u8bf7\u7ed9\u51fa\u6587\u732e\u51fa\u5904","num_tokens":56},{"role":"assistant","content":"Slater-Koster \u7d27\u675f\u7f1a\u6a21\u578b\uff08Slater and Koster, 1954\uff09\u662f\u91cf\u5b50\u5316\u5b66\u548c\u56fa\u4f53\u7269\u7406\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u6a21\u578b\uff0c\u7528\u4e8e\u8ba1\u7b97\u6676\u4f53\u4e2d\u7535\u5b50\u7684\u5e26\u7ed3\u6784\u3002\u8be5\u6a21\u578b\u901a\u8fc7\u53c2\u6570\u5316\u7684\u65b9\u6cd5\u7b80\u5316\u4e86\u6ce2\u51fd\u6570\u548c\u54c8\u5bc6\u987f\u91cf\u7684\u8ba1\u7b97\uff0c\u7279\u522b\u662f\u5728\u8ba1\u7b97\u539f\u5b50\u95f4\u7684\u8dc3\u8fc1\u79ef\u5206\u65f6\u3002\n\n\u81ea1954\u5e74\u63d0\u51fa\u4ee5\u6765\uff0c\u8fd9\u4e2a\u6a21\u578b\u786e\u5b9e\u7ecf\u5386\u4e86\u591a\u79cd\u6539\u8fdb\u548c\u62d3\u5c55\uff0c\u4ee5\u66f4\u597d\u5730\u63cf\u8ff0\u66f4\u590d\u6742\u7684\u7269\u7406\u73b0\u8c61\u548c\u6750\u6599\u7279\u6027\u3002\u8fd9\u4e9b\u6539\u8fdb\u4e3b\u8981\u96c6\u4e2d\u5728\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\uff1a\n\n1. **\u53c2\u6570\u5316\u65b9\u6cd5\u7684\u6539\u8fdb\uff1a** \u968f\u7740\u66f4\u591a\u5b9e\u9a8c\u6570\u636e\u548c\u7b2c\u4e00\u6027\u539f\u7406\u8ba1\u7b97\u7ed3\u679c\u7684\u53ef\u7528\u6027\uff0cSlater-Koster\u53c2\u6570\uff08\u5982\\(V_{pp\\pi}\\)\u548c\\(V_{pp\\sigma}\\)\u7b49\uff09\u5f97\u5230\u4e86\u66f4\u7cbe\u786e\u7684\u4fee\u6b63\u3002\n\n2. **\u8003\u8651\u66f4\u591a\u7684\u76f8\u4e92\u4f5c\u7528\uff1a** \u5728\u539f\u59cb\u7684Slater-Koster\u65b9\u6cd5\u4e2d\uff0c\u901a\u5e38\u53ea\u8003\u8651\u6700\u8fd1\u90bb\u548c\u6b21\u8fd1\u90bb\u7684\u76f8\u4e92\u4f5c\u7528\u3002\u540e\u7eed\u7684\u7814\u7a76\u6269\u5c55\u4e86\u8fd9\u4e00\u8303\u56f4\uff0c\u5305\u62ec\u66f4\u8fdc\u90bb\u5c45\u7684\u76f8\u4e92\u4f5c\u7528\uff0c\u4ee5\u66f4\u51c6\u786e\u5730\u63cf\u8ff0\u7535\u5b50\u7ed3\u6784\u3002\n\n3. **\u8f68\u9053\u7684\u6269\u5c55\uff1a** \u521d\u59cb\u6a21\u578b\u4e3b\u8981\u8003\u8651\u4e86\\(s\\)\u548c\\(p\\)\u8f68\u9053\uff0c\u540e\u7eed\u7684\u5de5\u4f5c\u5f15\u5165\u4e86\\(d\\)\u8f68\u9053\u548c\\(f\\)\u8f68\u9053\uff0c\u4ee5\u53ca\u8fd9\u4e9b\u8f68\u9053\u7684\u66f4\u590d\u6742\u76f8\u4e92\u4f5c\u7528\u3002\n\n4. **\u5bf9\u79f0\u6027\u548c\u62d3\u6251\u6548\u5e94\u7684\u8003\u8651\uff1a** \u968f\u7740\u5bf9\u79f0\u6027\u548c\u62d3\u6251\u7269\u7406\u7684\u7814\u7a76\u6df1\u5165\uff0c\u7d27\u675f\u7f1a\u6a21\u578b\u4e5f\u88ab\u62d3\u5c55\u5230\u5305\u62ec\u8fd9\u4e9b\u65b0\u5174\u6982\u5ff5\u3002\n\n5. **\u6570\u503c\u65b9\u6cd5\u7684\u6539\u8fdb\uff1a** \u4e3a\u4e86\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u548c\u7cbe\u5ea6\uff0c\u8bb8\u591a\u6570\u503c\u6280\u672f\u88ab\u5f15\u5165\u5230\u7d27\u675f\u7f1a\u6a21\u578b\u4e2d\uff0c\u4f8b\u5982\u81ea\u6d3d\u573a\u65b9\u6cd5\u7b49\u3002\n\n\u5173\u4e8e\u5177\u4f53\u7684\u6587\u732e\u51fa\u5904\uff0c\u8fd9\u91cc\u5217\u51fa\u4e00\u4e9b\u4ee3\u8868\u6027\u7684\u6539\u8fdb\u548c\u62d3\u5c55\uff1a\n\n- **Harrison, Walter A.** \"Electronic Structure and the Properties of Solids: The Physics of the Chemical Bond.\" Dover Publications, 1989. \n - Harrison\u5728\u8fd9\u672c\u4e66\u4e2d\u8be6\u7ec6\u8ba8\u8bba\u4e86Slater-Koster\u65b9\u6cd5\u7684\u62d3\u5c55\uff0c\u5305\u62ec\u66f4\u591a\u79cd\u7c7b\u7684\u8f68\u9053\u548c\u53c2\u6570\u5316\u65b9\u6cd5\u3002\n\n- **Papaconstantopoulos, Dimitrios A.** \"Handbook of the Band Structure of Elemental Solids.\" Plenum Press, New York, 1986.\n - \u8fd9\u672c\u624b\u518c\u63d0\u4f9b\u4e86\u5927\u91cf\u7684Slater-Koster\u53c2\u6570\uff0c\u8fd9\u4e9b\u53c2\u6570\u662f\u57fa\u4e8e\u5b9e\u9a8c\u548c\u7b2c\u4e00\u6027\u539f\u7406\u8ba1\u7b97\u5f97\u5230\u7684\uff0c\u4e3a\u5404\u79cd\u5143\u7d20\u7684\u7d27\u675f\u7f1a\u6a21\u578b\u63d0\u4f9b\u4e86\u6570\u636e\u652f\u6301\u3002\n\n- **J. C. Slater and G. F. Koster,** \"Simplified LCAO Method for the Periodic Potential Problem,\" Physical Review, 94, 1498-1524 (1954).\n - \u8fd9\u662f\u539f\u59cb\u7684Slater-Koster\u8bba\u6587\uff0c\u63d0\u51fa\u4e86\u7d27\u675f\u7f1a\u6a21\u578b\u7684\u57fa\u672c\u7406\u8bba\u3002\n\n- **Vogl, P., H. P. Hjalmarson, and J. D. Dow.** \"A semi-empirical tight-binding theory of the electronic structure of semiconductors.\" Journal of Physics and Chemistry of Solids 44.5 (1983): 365-378.\n - \u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u4f5c\u8005\u63d0\u51fa\u4e86\u4e00\u4e2a\u534a\u7ecf\u9a8c\u7684\u7d27\u675f\u7f1a\u6a21\u578b\uff0c\u7279\u522b\u662f\u5bf9\u534a\u5bfc\u4f53\u6750\u6599\u8fdb\u884c\u4e86\u8be6\u7ec6\u7684\u53c2\u6570\u5316\u7814\u7a76\u3002\n\n- **C. M. Goringe, D. R. Bowler, and E. Hern\u00e1ndez,** \"Tight-binding modelling of materials,\" Reports on Progress in Physics 60.12 (1997): 1447.\n - \u8fd9\u7bc7\u7efc\u8ff0\u6587\u7ae0\u8ba8\u8bba\u4e86\u7d27\u675f\u7f1a\u6a21\u578b\u7684\u5404\u79cd\u73b0\u4ee3\u5e94\u7528\uff0c\u5305\u62ec\u6570\u503c\u65b9\u6cd5\u7684\u6539\u8fdb\u548c\u66f4\u590d\u6742\u7cfb\u7edf\u7684\u6a21\u62df\u3002\n\n\u8fd9\u4e9b\u6587\u732e\u63d0\u4f9b\u4e86\u5bf9Slater-Koster\u7d27\u675f\u7f1a\u6a21\u578b\u4ece1954\u5e74\u4ee5\u6765\u7684\u5404\u79cd\u6539\u8fdb\u548c\u62d3\u5c55\u7684\u8be6\u7ec6\u6982\u8ff0\u3002","num_tokens":1069}],"turn":1,"language":"Chinese","category_tag":{"criteria_v0.1":{"specificity":true,"domain_knowledge":true,"complexity":true,"problem_solving":false,"creativity":false,"technical_accuracy":true,"real_world":true},"if_v0.1":{"if":true,"score":4},"math_v0.1":{"math":false}}}
{"question_id":11429,"category":"Instruction Following","model_a":"im-also-a-good-gpt2-chatbot","model_b":"gemini-1.5-pro-api-preview","winner":"tie","conversation_a":[{"role":"user","content":"\u8bf7\u5c06\u4e0b\u9762\u7684\u5185\u5bb9\u7ffb\u8bd1\u6210\u82f1\u6587\uff0c\u4e0d\u8981\u4f7f\u7528\u751f\u50fb\u8bcd\uff1a \u201c\u516c\u56ed\u4e8c\u5341\u5206\u949f\u6548\u5e94\u201d\u6307\u4e0d\u9700\u8981\u8fdb\u884c\u4efb\u4f55\u8fd0\u52a8\uff0c\u53ea\u9700\u8981\u5728\u57ce\u5e02\u516c\u56ed\u4f11\u606f\u4e8c\u5341\u5206\u949f\uff0c\u4eba\u5c31\u4f1a\u53d8\u5f97\u5fc3\u60c5\u6109\u60a6\u3002\u4ece\u4e3b\u89c2\u611f\u53d7\u89d2\u5ea6\u5206\u6790\uff0c\u6062\u590d\u6027\u73af\u5883\u7406\u8bba\uff08Attention Restoration Theory, ART\uff09\u548c\u751f\u7269\u4eb2\u548c\u7406\u8bba\uff08Biophilia Hypothesis\uff09\u8bc1\u660e\uff0c\u63a5\u89e6\u81ea\u7136\u73af\u5883\u6709\u5229\u4e8e\u4eba\u4eec\u653e\u677e\u7cbe\u795e\uff0c\u8fdb\u884c\u66f4\u591a\u60c5\u611f\u4ea4\u6d41\u3002\u4ece\u6fc0\u7d20\u89d2\u5ea6\u5206\u6790\uff0c\u5728\u57ce\u5e02\u516c\u56ed\u7684\u4f11\u606f\u53ef\u4ee5\u51cf\u5c11\u76ae\u8d28\u9187\u548cDHEA\u7684\u6d53\u5ea6\uff0c\u4ece\u800c\u7f13\u89e3\u4eba\u4eec\u7684\u538b\u529b\u3002","num_tokens":197},{"role":"assistant","content":"The \"20-Minute Park Effect\" refers to the phenomenon where simply resting in an urban park for 20 minutes, without any physical activity, can improve one's mood. From a subjective perspective, the Attention Restoration Theory (ART) and the Biophilia Hypothesis suggest that exposure to natural environments helps people relax and encourages more emotional interactions. From a hormonal perspective, resting in urban parks can reduce cortisol and DHEA levels, thereby alleviating stress.","num_tokens":93}],"conversation_b":[{"role":"user","content":"\u8bf7\u5c06\u4e0b\u9762\u7684\u5185\u5bb9\u7ffb\u8bd1\u6210\u82f1\u6587\uff0c\u4e0d\u8981\u4f7f\u7528\u751f\u50fb\u8bcd\uff1a \u201c\u516c\u56ed\u4e8c\u5341\u5206\u949f\u6548\u5e94\u201d\u6307\u4e0d\u9700\u8981\u8fdb\u884c\u4efb\u4f55\u8fd0\u52a8\uff0c\u53ea\u9700\u8981\u5728\u57ce\u5e02\u516c\u56ed\u4f11\u606f\u4e8c\u5341\u5206\u949f\uff0c\u4eba\u5c31\u4f1a\u53d8\u5f97\u5fc3\u60c5\u6109\u60a6\u3002\u4ece\u4e3b\u89c2\u611f\u53d7\u89d2\u5ea6\u5206\u6790\uff0c\u6062\u590d\u6027\u73af\u5883\u7406\u8bba\uff08Attention Restoration Theory, ART\uff09\u548c\u751f\u7269\u4eb2\u548c\u7406\u8bba\uff08Biophilia Hypothesis\uff09\u8bc1\u660e\uff0c\u63a5\u89e6\u81ea\u7136\u73af\u5883\u6709\u5229\u4e8e\u4eba\u4eec\u653e\u677e\u7cbe\u795e\uff0c\u8fdb\u884c\u66f4\u591a\u60c5\u611f\u4ea4\u6d41\u3002\u4ece\u6fc0\u7d20\u89d2\u5ea6\u5206\u6790\uff0c\u5728\u57ce\u5e02\u516c\u56ed\u7684\u4f11\u606f\u53ef\u4ee5\u51cf\u5c11\u76ae\u8d28\u9187\u548cDHEA\u7684\u6d53\u5ea6\uff0c\u4ece\u800c\u7f13\u89e3\u4eba\u4eec\u7684\u538b\u529b\u3002","num_tokens":197},{"role":"assistant","content":"## The \"20-Minute Park Effect\"\n\nThe \"20-minute park effect\" refers to the phenomenon where simply relaxing in a city park for 20 minutes, without any physical exercise, can significantly improve one's mood. \n\nFrom a subjective perspective, Attention Restoration Theory (ART) and the Biophilia Hypothesis support this effect. They suggest that exposure to natural environments helps people relax mentally and engage in more emotional communication. \n\nFrom a hormonal perspective, resting in a city park can reduce the levels of cortisol and DHEA, hormones associated with stress. This hormonal change contributes to the feeling of relaxation and stress relief.","num_tokens":126}],"turn":1,"language":"Chinese","category_tag":{"criteria_v0.1":{"specificity":false,"domain_knowledge":false,"complexity":false,"problem_solving":false,"creativity":false,"technical_accuracy":false,"real_world":false},"if_v0.1":{"if":true,"score":4},"math_v0.1":{"math":false}}}
{"question_id":608,"category":"Instruction Following","model_a":"command-r-plus","model_b":"mistral-large-2402","winner":"tie 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{"question_id":14493,"category":"Instruction Following","model_a":"claude-3-opus-20240229","model_b":"gpt-4-0125-preview","winner":"model_a","conversation_a":[{"role":"user","content":"ESSENZA Inc. is an all equity firm with EBIT = $510,000. Additionally, the firm plans to pay all its future earnings as cash dividends to its shareholders. The current return on Government of Canada T-Bills is 2.0%, the return on the market portfolio is 8.0% and ESSENZA's estimated beta is 0.8. According to M&M Case 1, What is the total market value of ESSENZA?","num_tokens":97},{"role":"assistant","content":"To determine the total market value of ESSENZA Inc. using the Modigliani and Miller (M&M) Proposition I under no taxes (Case 1), we need to calculate the company's cost of equity and use the present value of perpetuity formula. Let's solve this problem step by step.\n\nGiven:\n- ESSENZA Inc. is an all-equity firm.\n- EBIT = $510,000\n- The firm plans to pay all its future earnings as cash dividends to its shareholders.\n- Return on Government of Canada T-Bills (risk-free rate, Rf) = 2.0%\n- Return on the market portfolio (Rm) = 8.0%\n- ESSENZA's estimated beta (\u03b2) = 0.8\n\nStep 1: Calculate the cost of equity (Re) using the Capital Asset Pricing Model (CAPM).\nRe = Rf + \u03b2(Rm - Rf)\nRe = 2.0% + 0.8(8.0% - 2.0%)\nRe = 2.0% + 4.8% = 6.8%\n\nStep 2: Calculate the total market value of ESSENZA using the present value of perpetuity formula.\nAccording to M&M Proposition I (Case 1), the market value of an all-equity firm is equal to the present value of its expected future cash flows discounted at the cost of equity.\n\nSince ESSENZA plans to pay all its future earnings as cash dividends, we can use the EBIT as the expected future cash flow.\n\nMarket Value of ESSENZA = EBIT \u00f7 Re\nMarket Value of ESSENZA = $510,000 \u00f7 0.068\nMarket Value of ESSENZA \u2248 $7,500,000\n\nTherefore, according to M&M Proposition I (Case 1), the total market value of ESSENZA Inc. is approximately $7,500,000.","num_tokens":402}],"conversation_b":[{"role":"user","content":"ESSENZA Inc. is an all equity firm with EBIT = $510,000. Additionally, the firm plans to pay all its future earnings as cash dividends to its shareholders. The current return on Government of Canada T-Bills is 2.0%, the return on the market portfolio is 8.0% and ESSENZA's estimated beta is 0.8. According to M&M Case 1, What is the total market value of ESSENZA?","num_tokens":97},{"role":"assistant","content":"To estimate the total market value of ESSENZA Inc., which is an all-equity firm, we can use the Capital Asset Pricing Model (CAPM) to determine the cost of equity and then apply the dividend discount model (DDM), under the assumption of perpetual dividends as implied by the Modigliani and Miller Proposition I without taxes (M&M Case 1). Given that the firm plans to pay all its future earnings as dividends, we can simplify this to a model where the dividend payout is equal to its Earnings Before Interest and Taxes (EBIT), given no debt (all equity) and, thus, no interest expense.\n\nFirst, let's calculate the cost of equity (\\(r_e\\)) using the CAPM formula:\n\\[r_e = r_f + \\beta (r_m - r_f)\\]\n\nWhere:\n- \\(r_e\\) is the cost of equity,\n- \\(r_f\\) is the risk-free rate (return on Government of Canada T-Bills, 2.0% or 0.02),\n- \\(\\beta\\) is the beta of the firm (0.8),\n- \\(r_m\\) is the return on the market portfolio (8.0% or 0.08).\n\nPlugging in the numbers:\n\\[r_e = 0.02 + 0.8 * (0.08 - 0.02) = 0.02 + 0.8 * 0.06 = 0.02 + 0.048 = 0.068\\]\n\nSo, the cost of equity (\\(r_e\\)) is 6.8% or 0.068.\n\nNext, considering that ESSENZA Inc. plans to pay all its future earnings (EBIT) as dividends and using the perpetual dividend model (since no growth in dividends is mentioned), the value of the firm (\\(V\\)) can be calculated as follows:\n\\[V = \\frac{D}{r_e}\\]\n\nWhere:\n- \\(D\\) is the annual dividend payout, which in this case is equal to its EBIT of $510,000,\n- \\(r_e\\) is the cost of equity calculated above.\n\nTherefore:\n\\[V = \\frac{510,000}{0.068} = \\frac{510,000}{0.068} = \\$7,500,000\\]\n\nThus, under the assumptions given, the total market value of ESSENZA Inc. is approximately $7,500,000.","num_tokens":505}],"turn":1,"language":"English","category_tag":{"criteria_v0.1":{"specificity":true,"domain_knowledge":true,"complexity":true,"problem_solving":true,"creativity":false,"technical_accuracy":true,"real_world":true},"if_v0.1":{"if":true,"score":4},"math_v0.1":{"math":true}}}
{"question_id":10687,"category":"Instruction Following","model_a":"claude-3-sonnet-20240229","model_b":"mistral-large-2402","winner":"model_a","conversation_a":[{"role":"user","content":"In modern Deep Learning models, how is the choice of the activation motivated? Give detailed advice, at top machine learning expert level, on how to decide which activation function to use and when.","num_tokens":38},{"role":"assistant","content":"The choice of activation function in modern deep learning models is a crucial decision that can significantly impact the performance and behavior of the model. The activation function introduces non-linearity into the network, allowing it to learn complex patterns and mappings from the input data. Here's a detailed analysis and advice on how to choose the appropriate activation function:\n\n1. **Vanishing\/Exploding Gradients**: One of the primary considerations when selecting an activation function is its ability to mitigate the vanishing or exploding gradient problem. During the backpropagation process, gradients can either become extremely small (vanishing) or extremely large (exploding), leading to difficulties in training the model effectively. Activation functions like the Rectified Linear Unit (ReLU) and its variants (Leaky ReLU, ELU) are widely used because they help alleviate the vanishing gradient problem, allowing for more effective training of deep neural networks.\n\n2. **Non-linearity**: Deep learning models rely on non-linear activation functions to learn complex, non-linear mappings between inputs and outputs. Linear activation functions, such as the identity function, limit the model's ability to learn intricate patterns. Popular non-linear activation functions include the sigmoid, tanh, and ReLU families, each with its own characteristics and suitability for different problem domains.\n\n3. **Output Range**: The output range of the activation function can be an important factor, especially when working with specific data distributions or when the outputs need to be constrained to a particular range. For example, the sigmoid function outputs values between 0 and 1, making it suitable for binary classification tasks or when outputs need to be interpreted as probabilities. The tanh function, with an output range of -1 to 1, can be useful in scenarios where both positive and negative values are expected.\n\n4. **Sparsity and Biological Plausibility**: In certain applications, such as in convolutional neural networks for image processing, sparsity (i.e., having many zero-valued activations) can be desirable for computational efficiency and biological plausibility. The ReLU activation function introduces sparsity by setting all negative values to zero, making it a popular choice for convolutional layers.\n\n5. **Computational Efficiency**: The computational complexity of the activation function can be a consideration, especially in resource-constrained environments or when working with large models. The ReLU activation function is computationally efficient because it involves a simple thresholding operation, making it faster than other activation functions like sigmoid or tanh, which require more complex computations.\n\n6. **Gradient Behavior**: The behavior of the activation function's gradient can influence the training dynamics and convergence properties of the model. For example, the sigmoid and tanh functions have gradients that become very small (saturate) for large positive or negative inputs, which can slow down learning. The ReLU, on the other hand, has a constant gradient for positive inputs, which can lead to faster convergence in certain cases.\n\n7. **Problem Domain and Task**: The choice of activation function can also depend on the specific problem domain and task at hand. For example, in natural language processing tasks, the gating mechanism in recurrent neural networks often benefits from the use of sigmoid or tanh activations. In generative adversarial networks (GANs), the use of leaky ReLU or ELU activations can help stabilize training and improve the quality of generated samples.\n\nIn practice, the choice of activation function often involves empirical evaluation and experimentation. A common approach is to start with a well-established activation function like ReLU or its variants (e.g., Leaky ReLU, ELU) and then explore other options if the performance or convergence is unsatisfactory. It's also common to use different activation functions for different layers or components of the network, depending on the specific requirements and characteristics of the task.\n\nAdditionally, recent advances in deep learning have introduced novel activation functions, such as Swish, Mish, and GELU, which aim to combine the benefits of different activation functions while addressing specific limitations. These newer activation functions can be explored, especially in cases where traditional activations fall short or for specific problem domains where they have shown promising results.\n\nUltimately, the choice of activation function should be guided by a thorough understanding of the problem domain, the characteristics of the data, the model architecture, and the specific requirements of the task. Careful experimentation, evaluation, and fine-tuning are often necessary to find the optimal activation function or combination of activation functions for a given deep learning model.","num_tokens":901}],"conversation_b":[{"role":"user","content":"In modern Deep Learning models, how is the choice of the activation motivated? Give detailed advice, at top machine learning expert level, on how to decide which activation function to use and when.","num_tokens":38},{"role":"assistant","content":"In modern deep learning models, the choice of activation function is motivated by several factors, including the nature of the problem, the architecture of the network, computational efficiency, and the desire to mitigate issues like vanishing or exploding gradients. Here's a detailed advice on how to decide which activation function to use and when:\n\n1. **ReLU (Rectified Linear Unit)**: ReLU is the most commonly used activation function in deep learning models. It outputs the input directly if it is positive; otherwise, it outputs zero. ReLU is preferred for hidden layers in many modern networks due to its computational efficiency and its ability to mitigate the vanishing gradient problem. However, ReLU can cause \"dying neurons\" where some neurons stop responding to variations in input or weight updates.\n\n2. **Leaky ReLU and Parametric ReLU**: These are variants of ReLU that address the \"dying neurons\" problem. Leaky ReLU assigns a small negative slope to negative input values, while Parametric ReLU learns this slope as a parameter during training. These can be used when you're experiencing issues with ReLU like dying neurons.\n\n3. **Sigmoid and Tanh**: These functions are less commonly used in modern deep learning due to their susceptibility to the vanishing gradient problem. However, they can be useful in specific scenarios. For instance, the sigmoid function outputs values between 0 and 1, making it suitable for models where you need to interpret the output as a probability, like in binary classification problems. Tanh is a zero-centered version of the sigmoid, and it can be used in situations where you want the output to range from -1 to 1.\n\n4. **Softmax**: The softmax function is typically used in the output layer of a neural network for multi-class classification problems. It converts the outputs for each class into a probability distribution, where the probabilities of all possible outcomes add up to 1.\n\n5. **Softplus and Softsign**: These are smooth approximations to ReLU and the absolute value function, respectively. They can be used when you want a smooth, differentiable approximation to these functions.\n\n6. **Swish and Mish**: These are self-gated activation functions that have shown promising results in some recent studies. They are smooth and non-monotonic, which can make them more expressive than ReLU and its variants. They can be used when you want to experiment with more recent activation functions that might offer improved performance.\n\nIn conclusion, the choice of activation function depends on the specific problem, the architecture of your network, and the trade-offs you're willing to make in terms of computational efficiency, ease of optimization, and expressiveness. It's often a good idea to start with ReLU or one of its variants for the hidden layers and softmax for the output layer in a classification problem, and then experiment with other activation functions as needed.","num_tokens":581}],"turn":1,"language":"English","category_tag":{"criteria_v0.1":{"specificity":true,"domain_knowledge":true,"complexity":false,"problem_solving":true,"creativity":true,"technical_accuracy":true,"real_world":true},"if_v0.1":{"if":true,"score":4},"math_v0.1":{"math":false}}}
{"question_id":5140,"category":"Instruction Following","model_a":"gpt-3.5-turbo-0125","model_b":"yi-34b-chat","winner":"model_b","conversation_a":[{"role":"user","content":"Verbeter en optimaliseer in Julia de volgende functie function run() :: Nothing\n\n mini = 36 # minimum( hitScoreTabel )\n maxi = maximum( hitScoreTabel )\n hhhh = ( maxi - mini ) \u00f7 ( schaalverdeling - 2 )\n\n op[ 1 , 1 ] = 0\n op[ 2 , 1 ] = mini\n\n schaling( hhhh )\n vergelijkHitScoreTabelMetSchaalverdeling()\n\n #~ Sorteren zoneTabel op de 2e kolom\n data = sortslices( zoneTabel , dims = 1 , lt = ( x , y ) -> isless( x[ 2 ] , y[ 2 ] ) )\n afdruk()\n\n #~ Frequentietelling\n wordcounts = Dict{ String , Int }()\n for word in data[ : , 3 ]\n wordcounts[ word ] = get( wordcounts , word , 0 ) + 1\n end\n println()\n Keys = keys( wordcounts )\n Values = values( wordcounts )\n println( \"Sleutels = \" , Keys )\n println( \"Waardes = \" , Values , \" Som = \", sum( Values ))\n\n #~ Naar Meest \/ Minst\n bestand1 = open( \"Frank\/hitsMeest.txt\" , \"w\" )\n bestand2 = open( \"Frank\/hitsMinst.txt\" , \"w\" )\n for i \u2208 1 : JP.totaal\n if data[ i , 2 ] \u2264 ( mini + 3hhhh )\n schrijfweg( bestand1 , i , data )\n elseif data[ i , 2 ] \u2265 ( maxi - 7hhhh )\n schrijfweg( bestand2 , i , data )\n end\n end\n close( bestand1 )\n close( bestand2 )\n\nend","num_tokens":427},{"role":"assistant","content":"Hier is de verbeterde en geoptimaliseerde functie in Julia:\n\n```julia\nfunction run()::Nothing\n mini = minimum(hitScoreTabel)\n maxi = maximum(hitScoreTabel)\n hhhh = div(maxi - mini, schaalverdeling - 2)\n\n op = zeros(Int, 2, 1)\n op[2, 1] = mini\n\n schaling(hhhh)\n vergelijkHitScoreTabelMetSchaalverdeling()\n\n # Sorteren zoneTabel op de 2e kolom\n data = sortslices(zoneTabel, dims = 1, lt = (x, y) -> x[2] < y[2])\n afdruk()\n\n # Frequentietelling\n wordcounts = Dict{String, Int}()\n for word in data[:, 3]\n wordcounts[word] = get(wordcounts, word, 0) + 1\n end\n println()\n Keys = keys(wordcounts)\n Values = values(wordcounts)\n println(\"Sleutels = \", Keys)\n println(\"Waardes = \", Values, \" Som = \", sum(Values))\n\n # Naar Meest \/ Minst\n bestand1 = open(\"Frank\/hitsMeest.txt\", \"w\")\n bestand2 = open(\"Frank\/hitsMinst.txt\", \"w\")\n for i in 1:JP.totaal\n if data[i, 2] \u2264 mini + 3hhhh\n schrijfweg(bestand1, i, data)\n elseif data[i, 2] \u2265 maxi - 7hhhh\n schrijfweg(bestand2, i, data)\n end\n end\n close(bestand1)\n close(bestand2)\n return nothing\nend\n```\n\nIk heb enkele verbeteringen aangebracht, zoals het gebruik van `minimum()` in plaats van `minimum(hitScoreTabel)`, het initialiseren van `op` met de juiste grootte en type, en het aanpassen van de loop met `in` in plaats van `\u2208`. Deze aanpassingen zouden de functie beter moeten laten werken en optimaliseren. Let op dat je de ontbrekende functies zoals `schaling()`, `vergelijkHitScoreTabelMetSchaalverdeling()`, en `schrijfweg()` implementeert, aangezien deze functies niet in de verstrekte code zijn opgenomen.","num_tokens":543}],"conversation_b":[{"role":"user","content":"Verbeter en optimaliseer in Julia de volgende functie function run() :: Nothing\n\n mini = 36 # minimum( hitScoreTabel )\n maxi = maximum( hitScoreTabel )\n hhhh = ( maxi - mini ) \u00f7 ( schaalverdeling - 2 )\n\n op[ 1 , 1 ] = 0\n op[ 2 , 1 ] = mini\n\n schaling( hhhh )\n vergelijkHitScoreTabelMetSchaalverdeling()\n\n #~ Sorteren zoneTabel op de 2e kolom\n data = sortslices( zoneTabel , dims = 1 , lt = ( x , y ) -> isless( x[ 2 ] , y[ 2 ] ) )\n afdruk()\n\n #~ Frequentietelling\n wordcounts = Dict{ String , Int }()\n for word in data[ : , 3 ]\n wordcounts[ word ] = get( wordcounts , word , 0 ) + 1\n end\n println()\n Keys = keys( wordcounts )\n Values = values( wordcounts )\n println( \"Sleutels = \" , Keys )\n println( \"Waardes = \" , Values , \" Som = \", sum( Values ))\n\n #~ Naar Meest \/ Minst\n bestand1 = open( \"Frank\/hitsMeest.txt\" , \"w\" )\n bestand2 = open( \"Frank\/hitsMinst.txt\" , \"w\" )\n for i \u2208 1 : JP.totaal\n if data[ i , 2 ] \u2264 ( mini + 3hhhh )\n schrijfweg( bestand1 , i , data )\n elseif data[ i , 2 ] \u2265 ( maxi - 7hhhh )\n schrijfweg( bestand2 , i , data )\n end\n end\n close( bestand1 )\n close( bestand2 )\n\nend","num_tokens":427},{"role":"assistant","content":"function run() :: Nothing\n\n # Mini en maxi\n mini = minimum(hitScoreTabel)\n maxi = maximum(hitScoreTabel)\n hhhh = (maxi - mini) \u00f7 (schaalverdeling - 2)\n\n # Initieel opstarten van op\n op[1,1] = 0\n op[2,1] = mini\n\n # Schaalverdeling\n schaling(hhhh)\n vergelijkHitScoreTabelMetSchaalverdeling()\n\n # ZoneTabel sorteren op de 2e kolom\n data = sortslices(zoneTabel, dims = 1, lt = (x, y) -> isless(x[2], y[2]))\n afdruk()\n\n # Frequentietelling\n wordcounts = Dict{String, Int}()\n for word in data[:, 3]\n wordcounts[word] = get(wordcounts, word, 0) + 1\n end\n println()\n Keys = collect(keys(wordcounts))\n Values = collect(values(wordcounts))\n println(\"Sleutels = \", Keys)\n println(\"Waardes = \", Values, \" Som = \", sum(Values))\n\n # Meest en minst frequente hits\n bestand1 = open(\"Frank\/hitsMeest.txt\", \"w\")\n bestand2 = open(\"Frank\/hitsMinst.txt\", \"w\")\n for i in 1:JP.totaal\n if data[i, 2] <= (mini + 3hhhh)\n schrijfweg(bestand1, i, data)\n elseif data[i, 2] >= (maxi - 7hhhh)\n schrijfweg(bestand2, i, data)\n end\n end\n close(bestand1)\n close(bestand2)\n\nend\n\n# Functies die nog niet zijn gedefinieerd\nfunction schaling(hhhh)\n # Dit is een dummy-functie om de compiler te verlichten.\n # In deze functie zou de schaalverdeling moeten worden\n # toegepast op de hitScoreTabel.\nend\n\nfunction vergelijkHitScoreTabelMetSchaalverdeling()\n # Dit is een dummy-functie om de compiler te verlichten.\n # In deze functie zou de hitScoreTabel moeten worden\n # vergeleken met de schaalverdeling.\nend\n\nfunction schrijfweg(bestand, i, data)\n # Dit is een dummy-functie om de compiler te verlichten.\n # In deze functie zou de informatie over de zone met\n # de nummer i worden geschreven naar het bestand.\nend\n\nfunction afdruk()\n # Dit is een dummy-functie om de compiler te verlichten.\n # In deze functie zou de zoneTabel naar de console worden\n # afgedrukt.\nend\n\n# Voorbeeld van het gebruik van 'global' om een variabele\n# in het hoofdscript te declareren\nglobal hitScoreTabel = [ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]\nglobal zoneTabel = [ [1, 3, \"a\"], [2, 4, \"b\"], [3, 5, \"c\"] ]\nglobal schaalverdeling = 10\nglobal JP = nothing\n\n# Run de functie\nrun()\n\n# De code hieronder is alleen om de compiler te verlichten\n# en is niet vereist voor de oplossing van de opdracht.\nfunction test()\n # Testfunctie om te controleren of alles werkt zoals verwacht.\nend\n\ntest()\n\n# Einde van de code\nprintln(\"Code is voltooid.\")","num_tokens":829}],"turn":1,"language":"English","category_tag":{"criteria_v0.1":{"specificity":true,"domain_knowledge":true,"complexity":true,"problem_solving":true,"creativity":false,"technical_accuracy":true,"real_world":true},"if_v0.1":{"if":true,"score":4},"math_v0.1":{"math":true}}}
{"question_id":14838,"category":"Instruction Following","model_a":"phi-3-mini-128k-instruct","model_b":"reka-flash-21b-20240226","winner":"model_a","conversation_a":[{"role":"user","content":"my cat doesn't like to be held. no matter how much we tried getting her used to it, she just doesn't like it and keeps wanting to wiggle out. she is not aggressive and never scratches or bites, but i want to minimize her discomfort. i need to remove her from a room for her safety because it has been sprayed with pesticide. what is the most comfortable way to hold her?","num_tokens":82},{"role":"assistant","content":"1. Prepare a safe and comfortable environment: Before attempting to hold your cat, ensure the room is safe and free from potential hazards. Since the room has been sprayed with pesticide, it's best to keep your cat in a different, secure space.\n\n\n2. Use a calm and soothing voice: Speak to your cat in a soft, gentle tone to help her feel more at ease.\n\n\n3. Approach slowly: Make sure you move slowly and calmly to avoid startling your cat.\n\n\n4. Offer treats: Holding treats can be a great way to distract your cat and make the experience more positive.\n\n\n5. Use a towel or blanket: If your cat is still resistant, you can try wrapping her in a towel or blanket. This can help to limit her movements and make her feel more secure.\n\n\n6. Support her body: When you finally pick her up, make sure to support her body fully. Lift her from the chest to the hindquarters, never from the back or the stomach.\n\n\n7. Be patient: It may take some time for your cat to get used to being held. Don't rush the process, and always remember to respect her comfort levels.","num_tokens":232}],"conversation_b":[{"role":"user","content":"my cat doesn't like to be held. no matter how much we tried getting her used to it, she just doesn't like it and keeps wanting to wiggle out. she is not aggressive and never scratches or bites, but i want to minimize her discomfort. i need to remove her from a room for her safety because it has been sprayed with pesticide. what is the most comfortable way to hold her?","num_tokens":82},{"role":"assistant","content":"If your cat doesn't like being held, it's best to approach the situation with sensitivity and respect for her boundaries. Here's a step-by-step guide to safely and comfortably removing her from the room:\n\n1. **Gain her trust**: Spend more time with her, offering treats, toys, and positive reinforcement. This will help her associate you with positive experiences.\n\n2. **Use a carrier**: A pet carrier is the safest and most comfortable way to transport a cat. It provides a sense of security and control for the cat. Make sure the carrier is the right size for your cat and has a comfortable cushion or blanket inside.\n\n3. **Introduce the carrier**: Allow your cat to explore the carrier on her own by leaving the door open. Place her favorite toys and treats inside to encourage her to go in.\n\n4. **Lure her into the carrier**: Once your cat is comfortable with the carrier, gently lure her inside using treats or toys. You can also gently scoop her up and place her inside.\n\n5. **Close the carrier**: Once she's inside, close the carrier door slowly and quietly. Make sure it's secure but not too tight.\n\n6. **Transport her**: Carry the carrier with both hands, supporting the bottom and sides to keep it steady. Keep the carrier level and avoid sudden movements.\n\n7. **Release her**: Once you've reached the safe area, gently release your cat from the carrier. Offer her treats, toys, and praise to reassure her.\n\nRemember to be patient and gentle throughout the process, as this may take some time for your cat to get used to. Always prioritize her comfort and safety, and never force her into a situation that makes her uncomfortable.","num_tokens":344}],"turn":1,"language":"English","category_tag":{"criteria_v0.1":{"specificity":true,"domain_knowledge":false,"complexity":false,"problem_solving":true,"creativity":true,"technical_accuracy":false,"real_world":true},"if_v0.1":{"if":true,"score":4},"math_v0.1":{"math":false}}}
{"question_id":15420,"category":"Instruction Following","model_a":"gemini-1.5-pro-api-0514","model_b":"gemini-1.5-flash-api-0514","winner":"tie","conversation_a":[{"role":"user","content":"\u043f\u0440\u043e\u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0439 \u0441\u0440\u0430\u0431\u043e\u0442\u043a\u0438 sast \u0441\u043a\u0430\u043d\u0435\u0440\u0430 fortify. \u041f\u043e\u0447\u0435\u043c\u0443 \u044d\u0442\u043e false positive \u0443\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u0438?\n\u043e\u0442\u0432\u0435\u0442 \u0434\u0430\u0439 \u043d\u0430 \u0440\u0443\u0441\u0441\u043a\u043e\u043c \u044f\u0437\u044b\u043a\u0435.\n\napp\/src\/main\/AndroidManifest.xml, line 226 (Android Bad Practices: Missing\nComponent Permission)\nIssue Details\nKingdom: Security Features\nScan Engine: SCA (Configuration)\nSink Details\nSink:\nFile: app\/src\/main\/AndroidManifest.xml:226\nTaint Flags:\n223 \n227\n228 \n227\n228 Harry Potter y la Declaraci\u00f3n de la Renta\n2. Harry Potter y el Prisionero de Azkaban -> Harry Potter y el Prisionero del IRPF\n3. Harry Potter y el Caliz de Fuego -> Harry Potter y el Caliz de la Contabilidad\n4. Harry Potter y la Orden del F\u00e9nix -> Harry Potter y la Orden del Balance\n5. Harry Potter y el Misterio del Pr\u00edncipe -> Harry Potter y el Misterio del Impuesto sobre la Renta\n6. Harry Potter y las Reliquias de la Muerte -> Harry Potter y las Reliquias de la Herencia\n7. Harry Potter y la C\u00e1mara de los Secretos (ya mencionada) -> Harry Potter y la C\u00e1mara de Comercio\n\nTambi\u00e9n se me ocurren algunas variantes adicionales:\n\n* Harry Potter y el Arte de la Fiscalidad\n* Harry Potter y el Secreto de la Contabilidad Creativa\n* Harry Potter y la Batalla contra la Evasi\u00f3n Fiscal\n* Harry Potter y el Legado de la Planificaci\u00f3n Sucesoria\n* Harry Potter y la Sociedad de Auditores\n\nEspero que estas sugerencias te inspiren y te ayuden a crear m\u00e1s t\u00edtulos de broma en la oficina. \u00a1Divertido!","num_tokens":342},{"role":"user","content":"Dame m\u00e1s alternativas, intenta que salgan m\u00e1s temas jur\u00eddicos.","num_tokens":17},{"role":"assistant","content":"\u00a1Claro! Aqu\u00ed te dejo m\u00e1s alternativas con temas jur\u00eddicos:\n\n1. Harry Potter y la Piedra Filosofal -> Harry Potter y la Ley de Sociedades\n2. Harry Potter y el Prisionero de Azkaban -> Harry Potter y el Prisionero del Tribunal\n3. Harry Potter y el Caliz de Fuego -> Harry Potter y el Caliz de la Jurisprudencia\n4. Harry Potter y la Orden del F\u00e9nix -> Harry Potter y la Orden del Poder Judicial\n5. Harry Potter y el Misterio del Pr\u00edncipe -> Harry Potter y el Misterio del Testamento\n6. Harry Potter y las Reliquias de la Muerte -> Harry Potter y las Reliquias del Patrimonio\n7. Harry Potter y la C\u00e1mara de los Secretos (ya mencionada) -> Harry Potter y la C\u00e1mara de Comercio\n\nY algunas m\u00e1s:\n\n* Harry Potter y el Arte de la Litigaci\u00f3n\n* Harry Potter y el Secreto de la Due Diligence\n* Harry Potter y la Batalla contra la Competencia Desleal\n* Harry Potter y el Legado de la Sucesi\u00f3n Empresarial\n* Harry Potter y la Sociedad de Abogados\n* Harry Potter y el Caso de la Responsabilidad Civil\n* Harry Potter y el Misterio del Contrato\n* Harry Potter y la Ley de Propiedad Intelectual\n* Harry Potter y el Tribunal de Apelaciones\n* Harry Potter y el Arte de la Negociaci\u00f3n\n* Harry Potter y el Secreto de la Confidencialidad\n* Harry Potter y la Batalla contra la Corrupci\u00f3n\n\nEspero que estas sugerencias te ayuden a crear m\u00e1s t\u00edtulos de broma relacionados con temas jur\u00eddicos en la oficina. \u00a1Disfruta!","num_tokens":390}],"turn":2,"language":"Spanish","category_tag":{"criteria_v0.1":{"specificity":false,"domain_knowledge":true,"complexity":false,"problem_solving":false,"creativity":true,"technical_accuracy":false,"real_world":true},"if_v0.1":{"if":true,"score":4},"math_v0.1":{"math":false}}}
{"question_id":9039,"category":"Instruction Following","model_a":"reka-core-20240501","model_b":"gpt-4-turbo-2024-04-09","winner":"model_b","conversation_a":[{"role":"user","content":"Estoy creando un personaje para D&D. Quiero que sea un semielfo explorador de nivel 1 que en el futuro ser\u00e1 se\u00f1or de las bestias. Quiero seguir el est\u00e1ndar de criterios de D&D. Comenzamos con la descripci\u00f3n f\u00edsica del personaje, incluyendo edad, altura, peso, color de pelo, ojos, ropa, etc...","num_tokens":85},{"role":"assistant","content":"**Nombre:** Thalion Moonwhisper \n**Raza:** Semielfo \n**Clase:** Explorador (Nivel 1) \n**Edad:** 25 a\u00f1os \n**Altura:** 1.85 metros \n**Peso:** 80 kg \n**Color de Pelo:** Cabello largo y ondulado de un tono plateado, que refleja su herencia \u00e9lfica. \n**Color de Ojos:** Ojos profundos y expresivos de un verde esmeralda, con destellos dorados bajo la luz del sol. \n**Ropa:** Thalion viste de manera pr\u00e1ctica pero con un toque elegante, adecuada para su vida como explorador. Lleva una t\u00fanica verde oscuro con detalles en cuero marr\u00f3n, que le permite moverse con facilidad. Encima, porta una capa de color azul noche con el borde decorado en patrones que recuerdan a las hojas y ramas de los bosques. Sus botas son de cuero suave, ideales para caminar largas distancias sin hacer ruido. \n**Accesorios:** Alrededor de su cintura, Thalion lleva un cintur\u00f3n ancho donde cuelga su espada corta y un pu\u00f1al. En su mochila, siempre lleva un arco corto y una aljaba con flechas. Adem\u00e1s, tiene un peque\u00f1o odre de agua y una provisi\u00f3n de frutos secos y bayas para sus viajes. \n**Marcas Distintivas:** En su mejilla derecha, Thalion tiene una peque\u00f1a cicatriz en forma de hoja, recordatorio de una aventura pasada. Su piel tiene un tono dorado claro, herencia de su madre humana, y su postura es erguida y confiada, reflejo de su entrenamiento como explorador.\n\n**Personalidad:** Thalion es curioso por naturaleza, siempre dispuesto a explorar nuevos lugares y descubrir criaturas desconocidas. Tiene un profundo respeto por la naturaleza y las bestias que la habitan, buscando siempre entenderlas y protegerlas. Su habilidad para comunicarse con los animales le ha ganado el t\u00edtulo de \"Se\u00f1or de las Bestias\", un t\u00edtulo que aspira a merecer completamente a lo largo de sus aventuras.\n\n**Historia:** Nacido en el coraz\u00f3n de un vasto bosque, Thalion creci\u00f3 entre los \u00e1rboles y las criaturas que lo habitaban. Sus padres, un elfo guardi\u00e1n de la naturaleza y una humana exploradora, le ense\u00f1aron los caminos de la tierra y el respeto por todos sus habitantes. Desde joven, Thalion demostr\u00f3 una habilidad innata para comunicarse con los animales, lo que le vali\u00f3 la amistad de muchas criaturas. Con el tiempo, decidi\u00f3 emprender viajes m\u00e1s all\u00e1 de su hogar, buscando aprender todo lo posible sobre el mundo y sus misterios, con la esperanza de alg\u00fan d\u00eda convertirse en un verdadero Se\u00f1or de las Bestias.","num_tokens":689}],"conversation_b":[{"role":"user","content":"Estoy creando un personaje para D&D. Quiero que sea un semielfo explorador de nivel 1 que en el futuro ser\u00e1 se\u00f1or de las bestias. Quiero seguir el est\u00e1ndar de criterios de D&D. Comenzamos con la descripci\u00f3n f\u00edsica del personaje, incluyendo edad, altura, peso, color de pelo, ojos, ropa, etc...","num_tokens":85},{"role":"assistant","content":"\u00a1Genial! Crear un personaje para Dungeons & Dragons es siempre emocionante, especialmente uno tan interesante como un semielfo explorador que aspira a ser un se\u00f1or de las bestias. Aqu\u00ed te ayudo a desarrollar la descripci\u00f3n f\u00edsica y algunos detalles de tu personaje:\n\n### Nombre\nPrimero, necesitas un nombre que refleje tanto su herencia \u00e9lfica como humana. Algunas sugerencias podr\u00edan ser: Elirion, Caelthas, Arya, o Tamior.\n\n### Descripci\u00f3n F\u00edsica\n- **Edad:** Los semielfos alcanzan la madurez alrededor de los 20 a\u00f1os y pueden vivir hasta los 180 a\u00f1os. Podr\u00edas elegir que tu personaje tenga alrededor de 25 a\u00f1os, joven pero con suficiente experiencia como para empezar su aventura.\n- **Altura y Peso:** Los semielfos tienden a estar entre los humanos y los elfos en t\u00e9rminos de estatura y complexi\u00f3n. Podr\u00eda medir alrededor de 5'9\" (175 cm) y pesar aproximadamente 150 lbs (68 kg), d\u00e1ndole una apariencia \u00e1gil y resistente, ideal para un explorador.\n- **Color de Pelo y Ojos:** Podr\u00eda tener el cabello largo y liso, t\u00edpicamente de color oscuro, como un casta\u00f1o profundo o negro, heredado de su lado \u00e9lfico. Sus ojos podr\u00edan ser de un verde vibrante o azul claro, rasgos distintivos de su herencia \u00e9lfica.\n- **Ropa y Apariencia:** Como explorador, su ropa ser\u00eda funcional y adecuada para el viaje a trav\u00e9s de diversos terrenos. Podr\u00eda llevar una capa de cuero con capucha para protegerse del clima, botas resistentes, y ropas en tonos verdes y marrones que le ayuden a camuflarse en entornos naturales. Una armadura ligera de cuero ser\u00eda adecuada, permiti\u00e9ndole movilidad y protecci\u00f3n.\n\n### Caracter\u00edsticas y Rasgos\n- **Personalidad:** Puedes elegir que sea curioso y amable, con un genuino amor por la naturaleza y las criaturas que la habitan. Esta pasi\u00f3n es lo que lo impulsa a convertirse en se\u00f1or de las bestias.\n- **Antecedentes:** Podr\u00eda tener un trasfondo como el de un \"Heredero del Guardabosques\", que indica que ha crecido en \u00e1reas silvestres y ha sido entrenado en las habilidades de supervivencia y rastreo. Esta experiencia le proporciona habilidades \u00fatiles como rastreador y protector de la naturaleza.\n- **Objetivos y Motivaciones:** Uno de sus objetivos principales podr\u00eda ser el de establecer un santuario para las criaturas m\u00e1gicas y raras, protegiendo a los animales y aprendiendo de ellos. Tambi\u00e9n podr\u00eda estar en busca de una criatura espec\u00edfica que le fascine o que haya visto en sus visiones o sue\u00f1os.\n\nCon estos detalles, tienes un personaje robusto y listo para aventurarse en el mundo de D&D. Recuerda que cada detalle puede ser ajustado a medida que el personaje evoluciona y creces en tu campa\u00f1a. \u00a1Divi\u00e9rtete jugando con \u00e9l!","num_tokens":748}],"turn":1,"language":"Spanish","category_tag":{"criteria_v0.1":{"specificity":true,"domain_knowledge":true,"complexity":false,"problem_solving":false,"creativity":false,"technical_accuracy":true,"real_world":true},"if_v0.1":{"if":true,"score":4},"math_v0.1":{"math":false}}}