{"id":98664,"date":"2026-09-01T09:25:08","date_gmt":"2026-09-01T01:25:08","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/98664.html"},"modified":"2026-09-01T09:25:08","modified_gmt":"2026-09-01T01:25:08","slug":"llm%e4%ba%8b%e4%bb%b6%e6%8e%a8%e7%90%86%e4%b8%8e%e6%95%b0%e5%80%bc%e9%a2%84%e6%b5%8b%e7%9a%84%e5%8d%8f%e5%90%8c%e6%9c%ba%e5%88%b6%ef%bc%9a%e6%8b%86%e8%a7%a3%e3%80%81%e9%87%8f%e5%8c%96%e4%b8%8e","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/98664.html","title":{"rendered":"LLM\u4e8b\u4ef6\u63a8\u7406\u4e0e\u6570\u503c\u9884\u6d4b\u7684\u534f\u540c\u673a\u5236\uff1a\u62c6\u89e3\u3001\u91cf\u5316\u4e0e\u8f7b\u91cf\u5316\u66ff\u4ee3\u65b9\u6848"},"content":{"rendered":"

\u5927\u578b\u8bed\u8a00\u6a21\u578b（LLM）\u5728\u9500\u91cf\u9884\u6d4b\u4e2d\u7684\u4ef7\u503c\u5df2\u83b7\u5b9e\u8bc1\u9a8c\u8bc1，\u4f46\u5176\u5185\u90e8\u8d21\u732e\u673a\u5236\u5c1a\u4e0d\u6e05\u6670\u2014\u2014LLM\u7684\u63a8\u7406\u80fd\u529b\u5982\u4f55\u8f6c\u5316\u4e3a\u6570\u503c\u9884\u6d4b\u7684\u7cbe\u5ea6\u63d0\u5347？\u672c\u6587\u7cfb\u7edf\u6027\u5730\u62c6\u89e3LLM\u5728\u9884\u6d4b\u7cfb\u7edf\u4e2d\u7684\u4e09\u4e2a\u6838\u5fc3\u7ec4\u4ef6\u2014\u2014\u4e8b\u4ef6\u63d0\u53d6\u3001\u6587\u672c\u6458\u8981\u4e0e\u8bed\u4e49\u5bf9\u9f50，\u901a\u8fc7\u6d88\u878d\u5b9e\u9a8c\u91cf\u5316\u5404\u7ec4\u4ef6\u7684\u72ec\u7acb\u8d21\u732e\u3002\u7814\u7a76\u8868\u660e，LLM\u7684\u6700\u4f73\u89d2\u8272\u662f\u201c\u4e8b\u4ef6\u7406\u89e3\u5668\u201d\u800c\u975e\u201c\u6570\u503c\u9884\u6d4b\u5668\u201d：\u5c06LLM\u7684\u4e8b\u4ef6\u63a8\u7406\u4e0e\u4e13\u7528\u65f6\u5e8f\u9884\u6d4b\u6a21\u5757\u89e3\u8026，\u53ef\u4f7f\u4e8b\u4ef6\u9a71\u52a8\u671f\u95f4\u7684\u9884\u6d4bMAE\u964d\u4f4e57.0%，MSE\u964d\u4f4e83.3%，\u800c\u76f4\u63a5\u4f7f\u7528LLM\u8fdb\u884c\u6570\u503c\u9884\u6d4b\u5219\u9762\u4e34\u7cbe\u5ea6\u635f\u5931\u4e0e\u91cf\u5316\u8bef\u5dee\u7684\u4e25\u91cd\u95ee\u9898\u3002\u672c\u6587\u8fdb\u4e00\u6b65\u63a2\u7d22\u8f7b\u91cf\u7ea7\u4e8b\u4ef6\u63a8\u7406\u6a21\u5757\u7684\u53ef\u884c\u6027，\u4ee5\u89c4\u5219\u5f15\u64ce\u6216\u5c0f\u53c2\u6570\u8bed\u8a00\u6a21\u578b（SLM）\u66ff\u4ee3\u5927\u53c2\u6570LLM，\u8bc4\u4f30\u7cbe\u5ea6-\u6548\u7387\u7684\u6743\u8861\u3002\u57fa\u4e8e\u4e0a\u8ff0\u5206\u6790，\u672c\u6587\u6784\u5efa\u201c\u4e8b\u4ef6\u63a8\u7406\u8d28\u91cf\u201d\u7684\u591a\u7ef4\u5ea6\u91cf\u5316\u6307\u6807\u4f53\u7cfb，\u5efa\u7acb\u63a8\u7406\u8d28\u91cf\u4e0e\u9884\u6d4b\u7cbe\u5ea6\u4e4b\u95f4\u7684\u6620\u5c04\u5173\u7cfb，\u4e3a\u8d44\u6e90\u53d7\u9650\u573a\u666f\u4e0b\u7684\u5de5\u4e1a\u90e8\u7f72\u63d0\u4f9b\u53ef\u64cd\u4f5c\u7684\u7406\u8bba\u6307\u5bfc\u3002<\/p>\n

\u5173\u952e\u8bcd：LLM\u4e8b\u4ef6\u63a8\u7406\u3001\u9500\u91cf\u9884\u6d4b\u3001\u6d88\u878d\u5b9e\u9a8c\u3001\u8f7b\u91cf\u5316\u90e8\u7f72\u3001\u63a8\u7406\u8d28\u91cf\u91cf\u5316<\/p>\n

1 \u5f15\u8a00<\/p>\n

2025\u81f32026\u5e74，\u5927\u578b\u8bed\u8a00\u6a21\u578b（LLM）\u4e0e\u9500\u91cf\u9884\u6d4b\u7684\u878d\u5408\u53d6\u5f97\u4e86\u7a81\u7834\u6027\u8fdb\u5c55\u3002EventCast\u6846\u67b6\u9996\u6b21\u7cfb\u7edf\u6027\u5730\u9a8c\u8bc1\u4e86LLM\u5728\u9700\u6c42\u9884\u6d4b\u4e2d\u7684\u5de5\u4e1a\u4ef7\u503c：\u5728\u8de8\u8d8a4\u4e2a\u56fd\u5bb6160\u4e2a\u5730\u533a\u7684\u771f\u5b9e\u7535\u5546\u573a\u666f\u4e2d\u90e8\u7f7210\u4e2a\u6708\u540e，EventCast\u5728\u4e8b\u4ef6\u9a71\u52a8\u671f\u95f4\u76f8\u6bd4\u6700\u4f18\u5de5\u4e1a\u57fa\u7ebf\u5b9e\u73b0\u4e86MAE\u964d\u4f4e57.0%，MSE\u964d\u4f4e83.3%\u3002\u8be5\u6846\u67b6\u81ea2025\u5e743\u6708\u8d77\u5df2\u90e8\u7f72\u4e8e\u5de5\u4e1a\u751f\u4ea7\u73af\u5883\u3002<\/p>\n

\u7136\u800c，EventCast\u7684\u6210\u529f\u63ed\u793a\u4e86\u4e00\u4e2a\u66f4\u6df1\u5c42\u7684\u7406\u8bba\u95ee\u9898：LLM\u7684\u63a8\u7406\u80fd\u529b\u7a76\u7adf\u5982\u4f55\u8f6c\u5316\u4e3a\u6570\u503c\u9884\u6d4b\u7684\u7cbe\u5ea6\u63d0\u5347？ \u8fd9\u4e00\u95ee\u9898\u76f4\u63a5\u5173\u7cfb\u5230LLM\u5728\u9884\u6d4b\u7cfb\u7edf\u4e2d\u7684\u89d2\u8272\u5b9a\u4f4d\u4e0e\u67b6\u6784\u8bbe\u8ba1\u3002<\/p>\n

\u66f4\u503c\u5f97\u5173\u6ce8\u7684\u662f，EventCast\u7684\u5b9e\u9a8c\u8bc1\u636e\u8868\u660e：LLM\u7528\u4e8e\u4e8b\u4ef6\u63a8\u7406\u800c\u975e\u6570\u503c\u9884\u6d4b\u662f\u66f4\u4f18\u7684\u8bbe\u8ba1\u9009\u62e9\u3002\u76f4\u63a5\u4f7f\u7528LLM\u8fdb\u884c\u6570\u503c\u9884\u6d4b\u7684\u65b9\u6cd5（\u5982Time-LLM\u3001OneFitsAll）\u5728\u4e8b\u4ef6\u9a71\u52a8\u671f\u95f4\u7684\u8868\u73b0\u663e\u8457\u52a3\u4e8eEventCast\u2014\u2014EventCast\u76f8\u6bd4Time-LLM\u5b9e\u73b082.4%\u7684MAE\u964d\u4f4e\u548c96.0%\u7684MSE\u964d\u4f4e\u3002\u7814\u7a76\u5c06\u6b64\u5f52\u56e0\u4e8eLLM\u67b6\u6784\u4e0e\u6570\u503c\u9884\u6d4b\u9700\u6c42\u4e4b\u95f4\u7684\u6839\u672c\u6027\u5931\u914d：（1）\u6570\u503c\u5fc5\u987btokenization，\u635f\u5931\u7cbe\u5ea6\u5e76\u5f15\u5165\u91cf\u5316\u4f2a\u5f71；（2）\u81ea\u56de\u5f52\u751f\u6210\u8fc7\u7a0b\u5728\u9884\u6d4b\u89c6\u754c\u4e0a\u7d2f\u79ef\u8bef\u5dee\u3002<\/p>\n

\u8fd9\u4e00\u53d1\u73b0\u5f15\u51fa\u4e86\u4e09\u4e2a\u6838\u5fc3\u7814\u7a76\u95ee\u9898：（1）LLM\u4e8b\u4ef6\u63a8\u7406\u5404\u7ec4\u4ef6（\u4e8b\u4ef6\u63d0\u53d6\u3001\u6587\u672c\u6458\u8981\u3001\u8bed\u4e49\u5bf9\u9f50）\u7684\u72ec\u7acb\u8d21\u732e\u5206\u522b\u662f\u4ec0\u4e48？（2）\u80fd\u5426\u6784\u5efa\u201c\u4e8b\u4ef6\u63a8\u7406\u8d28\u91cf\u201d\u7684\u91cf\u5316\u6307\u6807\u4f53\u7cfb？（3）\u662f\u5426\u5b58\u5728\u8f7b\u91cf\u7ea7\u7684\u4e8b\u4ef6\u63a8\u7406\u6a21\u5757\u66ff\u4ee3\u5927\u53c2\u6570LLM？<\/p>\n

2 \u76f8\u5173\u7814\u7a76<\/p>\n

2.1 EventCast：LLM\u4e8b\u4ef6\u63a8\u7406\u7684\u5de5\u4e1a\u9a8c\u8bc1<\/p>\n

EventCast\u63d0\u51fa\u4e86\u4e00\u4e2a\u6a21\u5757\u5316\u9884\u6d4b\u6846\u67b6：LLM\u4ec5\u7528\u4e8e\u4e8b\u4ef6\u9a71\u52a8\u7684\u63a8\u7406，\u975e\u7ed3\u6784\u5316\u4e1a\u52a1\u6570\u636e（\u4fc3\u9500\u6d3b\u52a8\u3001\u5047\u65e5\u5b89\u6392\u3001\u5356\u5bb6\u6fc0\u52b1）\u7ecfLLM\u5904\u7406\u8f6c\u5316\u4e3a\u53ef\u89e3\u91ca\u7684\u6587\u672c\u6458\u8981，\u518d\u4e0e\u5386\u53f2\u9700\u6c42\u7279\u5f81\u5728\u53cc\u5854\u67b6\u6784\u4e2d\u878d\u5408\u3002<\/p>\n

EventCast\u7684\u5173\u952e\u8bbe\u8ba1\u539f\u5219\u662f\u5173\u6ce8\u70b9\u5206\u79bb：LLM\u5904\u7406\u5176\u64c5\u957f\u7684\u4e8b\u52a1（\u7406\u89e3\u590d\u6742\u4e8b\u4ef6\u63cf\u8ff0\u3001\u63a8\u7406\u4e1a\u52a1\u4e0a\u4e0b\u6587\u3001\u751f\u6210\u7ed3\u6784\u5316\u8bed\u4e49\u6458\u8981），\u4e13\u7528\u9884\u6d4b\u6a21\u5757\u5904\u7406\u6570\u503c\u9884\u6d4b\u3002\u8fd9\u4e00\u201c\u5206\u9694\u201d\u5141\u8bb8\u6bcf\u4e2a\u7ec4\u4ef6\u5728\u5176\u539f\u751f\u9886\u57df\u8fd0\u4f5c，\u907f\u514d\u76f4\u63a5LLM\u9884\u6d4b\u65b9\u6cd5\u4e2d\u56f0\u6270\u7cfb\u7edf\u7684\u963b\u6297\u5931\u914d\u95ee\u9898\u3002LLM\u7684\u8f93\u51fa\u4f5c\u4e3a\u8bed\u4e49\u5148\u9a8c\u5f15\u5bfc\u9884\u6d4b\u5668，\u800c\u975e\u8bd5\u56fe\u66ff\u4ee3\u5b83\u3002<\/p>\n

2.2 LLM\u4e8b\u4ef6\u63a8\u7406\u5728\u9884\u6d4b\u4e2d\u7684\u62d3\u5c55<\/p>\n

Perntoft\u548cAtle（2026）\u8fdb\u4e00\u6b65\u5c06LLM\u8bbe\u8ba1\u4e3a\u6218\u7565\u7f16\u6392\u5668\u800c\u975e\u76f4\u63a5\u6570\u503c\u9884\u6d4b\u5668：\u6240\u6709\u6570\u503c\u8ba1\u7b97\u59d4\u6258\u7ed9\u5df2\u9a8c\u8bc1\u7684\u7edf\u8ba1\u5b9e\u73b0\u5e93，LLM\u5bf9\u6587\u672c\u4e8b\u4ef6\u63cf\u8ff0\u3001\u76ee\u6807\u5e8f\u5217\u81ea\u8eab\u5386\u53f2\u4e2d\u7684\u7c7b\u6bd4\u4ee5\u53ca\u8de8\u9886\u57df\u5148\u4f8b\u6848\u4f8b\u8fdb\u884c\u63a8\u7406\u3002\u5728\u6d88\u878d\u7814\u7a76\u4e2d，\u5b8c\u6574\u6d41\u6c34\u7ebf\u76f8\u5bf9\u4e8e\u65e0\u589e\u5f3a\u57fa\u7ebf\u5c06sMAPE\u964d\u4f4e\u4e8659%（\u5065\u5eb7\u4e2d\u5fc3）\u548c66%（\u7269\u6d41），\u63a8\u7406\u8d28\u91cf\u4e0e\u9884\u6d4b\u7cbe\u5ea6\u4e4b\u95f4\u5b58\u5728\u6e05\u6670\u7684\u6620\u5c04\u5173\u7cfb\u2014\u2014LLM\u4e8b\u4ef6\u63a8\u7406\u9700\u8981\u81f3\u5c11\u4e00\u4e2agrounding\u6765\u6e90\u624d\u80fd\u53ef\u9760\u8fd0\u4f5c，\u4e3b\u5bfc\u6765\u6e90\u7531\u53ef\u7528\u8bc1\u636e\u4e0e\u6d4b\u8bd5\u671f\u4e8b\u4ef6\u4e4b\u95f4\u7684\u7ed3\u6784\u5339\u914d\u5ea6\u51b3\u5b9a\u3002<\/p>\n

2.3 \u4e8b\u4ef6\u63a8\u7406\u8d28\u91cf\u7684\u8bc4\u4f30\u6846\u67b6<\/p>\n

WorldReasoner\u63d0\u4f9b\u4e86\u8bc4\u4f30LLM\u667a\u80fd\u4f53\u4e8b\u4ef6\u9884\u6d4b\u7684\u6846\u67b6，\u4ece\u4e09\u4e2a\u4e92\u8865\u8f74\u8fdb\u884c\u8bc4\u5206：\u7ed3\u679c\u8d28\u91cf（\u9488\u5bf9\u5df2\u89e3\u51b3\u7b54\u6848）\u3001\u8bc1\u636e\u8d28\u91cf（\u9488\u5bf9\u5f15\u7528\u7684\u6765\u6e90）\u548c\u63a8\u7406\u8d28\u91cf（\u9488\u5bf9\u4e8b\u540e hindsight\u56fe）\u3002\u5728\u516d\u4e2a\u53d7\u63a7\u667a\u80fd\u4f53\u8bbe\u7f6e\u4e2d，\u65f6\u95f4\u6709\u6548\u68c0\u7d22\u662f\u7ed3\u679c\u51c6\u786e\u6027\u7684\u6700\u5f3a\u9a71\u52a8\u56e0\u7d20；\u56e0\u679c\u56fe\u6784\u5efa\u6539\u5584\u4e86\u5173\u952e\u4e8b\u4ef6\u6062\u590d；\u542f\u7528\u56fe\u7684\u6b63\u786e\u9884\u6d4b\u66f4\u7262\u56fa\u5730\u57fa\u4e8e\u5173\u952e\u4e8b\u4ef6\u548c\u76f8\u5173\u6765\u6e90，\u4f46\u667a\u80fd\u4f53\u4ecd\u96be\u4ee5\u5c06\u57fa\u4e8e\u8bc1\u636e\u7684\u9884\u6d4b\u8f6c\u5316\u4e3a\u6821\u51c6\u826f\u597d\u7684\u6982\u7387\u3002<\/p>\n

3 LLM\u4e8b\u4ef6\u63a8\u7406\u7ec4\u4ef6\u7684\u6d88\u878d\u5206\u6790<\/p>\n

3.1 \u7ec4\u4ef6\u62c6\u89e3\u4e0e\u529f\u80fd\u5b9a\u4e49<\/p>\n

\u57fa\u4e8eEventCast\u7684\u6a21\u5757\u5316\u8bbe\u8ba1，LLM\u4e8b\u4ef6\u63a8\u7406\u5728\u9884\u6d4b\u7cfb\u7edf\u4e2d\u53ef\u62c6\u89e3\u4e3a\u4e09\u4e2a\u6838\u5fc3\u7ec4\u4ef6：<\/p>\n

\u7ec4\u4ef6A：\u4e8b\u4ef6\u63d0\u53d6（Event Extraction） \u3002\u4ece\u975e\u7ed3\u6784\u5316\u4e1a\u52a1\u6570\u636e（\u4fc3\u9500\u6587\u6848\u3001\u5047\u65e5\u5b89\u6392\u3001\u5356\u5bb6\u6fc0\u52b1）\u4e2d\u8bc6\u522b\u5e76\u63d0\u53d6\u76f8\u5173\u4e8b\u4ef6\u4fe1\u606f\u3002\u6838\u5fc3\u529f\u80fd\u4e3a\u4ece\u6587\u672c\u4e2d\u62bd\u53d6\u4e8b\u4ef6\u7c7b\u578b\u3001\u65f6\u95f4\u7a97\u53e3\u3001\u5f71\u54cd\u8303\u56f4\u7b49\u7ed3\u6784\u5316\u8981\u7d20\u3002<\/p>\n

\u7ec4\u4ef6B：\u6587\u672c\u6458\u8981（Textual Summarization） \u3002\u5c06\u539f\u59cb\u4e1a\u52a1\u6587\u672c\u8f6c\u5316\u4e3a\u7b80\u6d01\u3001\u53ef\u89e3\u91ca\u7684\u6587\u672c\u6458\u8981，\u5229\u7528LLM\u7684\u4e16\u754c\u77e5\u8bc6\u5904\u7406\u6587\u5316\u5dee\u5f02\u548c\u65b0\u9896\u4e8b\u4ef6\u7ec4\u5408\u3002<\/p>\n

\u7ec4\u4ef6C：\u8bed\u4e49\u5bf9\u9f50（Semantic Alignment） \u3002\u5c06\u6587\u672c\u6458\u8981\u4e0e\u5386\u53f2\u9700\u6c42\u7279\u5f81\u5728\u7edf\u4e00\u8868\u793a\u7a7a\u95f4\u4e2d\u5bf9\u9f50，\u4f7f\u4e8b\u4ef6\u8bed\u4e49\u4fe1\u606f\u80fd\u591f\u6709\u6548\u878d\u5165\u65f6\u5e8f\u9884\u6d4b\u6a21\u5757\u3002<\/p>\n

3.2 \u6d88\u878d\u5b9e\u9a8c\u8bbe\u8ba1<\/p>\n

\u8bbe\u8ba1\u4ee5\u4e0b\u6d88\u878d\u6761\u4ef6\u4ee5\u7cfb\u7edf\u8bc4\u4f30\u5404\u7ec4\u4ef6\u7684\u72ec\u7acb\u8d21\u732e：<\/p>\n

\u6761\u4ef6 \u4e8b\u4ef6\u63d0\u53d6 \u6587\u672c\u6458\u8981 \u8bed\u4e49\u5bf9\u9f50 \u9884\u6d4b\u6a21\u5757
\nFull \u2713 \u2713 \u2713 \u53cc\u5854\u67b6\u6784
\n-A \u2717（\u4eba\u5de5\u4e8b\u4ef6\u6807\u7b7e） \u2713 \u2713 \u53cc\u5854\u67b6\u6784
\n-B \u2713 \u2717（\u76f4\u63a5\u62fc\u63a5\u539f\u59cb\u6587\u672c） \u2713 \u53cc\u5854\u67b6\u6784
\n-C \u2713 \u2713 \u2717（\u4e8b\u4ef6\u7279\u5f81\u4e0e\u65f6\u5e8f\u7279\u5f81\u72ec\u7acb） \u53cc\u5854\u67b6\u6784
\n-LLM \u2717 \u2717 \u2717 \u53cc\u5854\u67b6\u6784（\u65e0\u4e8b\u4ef6\u77e5\u8bc6）
\nDirect-LLM \u2717 \u2713（LLM\u76f4\u63a5\u8f93\u51fa\u6570\u503c） \u2717 LLM\u81ea\u56de\u5f52<\/p>\n

\u53c2\u8003EventCast\u7684\u5b9e\u8bc1\u6846\u67b6，\u8bc4\u4f30\u5e94\u5728\u591a\u4e2a\u9884\u6d4b\u89c6\u754c（Horizon 1-4）\u4e0b\u8fdb\u884c，\u91cd\u70b9\u5206\u6790\u4e8b\u4ef6\u9a71\u52a8\u671f\u95f4\u7684\u8868\u73b0\u3002<\/p>\n

3.3 \u9884\u671f\u8d21\u732e\u91cf\u5316<\/p>\n

\u57fa\u4e8eEventCast\u7684\u5df2\u6709\u5b9e\u8bc1：<\/p>\n

\u00b7 \u7ec4\u4ef6B（\u6587\u672c\u6458\u8981）\u8d21\u732e\u6700\u5927：LLM\u5c06\u539f\u59cb\u4e1a\u52a1\u6570\u636e\u8f6c\u5316\u4e3a\u53ef\u89e3\u91ca\u6458\u8981，\u5229\u7528\u4e16\u754c\u77e5\u8bc6\u5904\u7406\u6587\u5316\u7ec6\u5fae\u5dee\u5f02\u3002\u5728\u4e8b\u4ef6\u9a71\u52a8\u671f\u95f4，\u6709\u4e8b\u4ef6\u77e5\u8bc6\u7684\u53d8\u4f53\u6bd4\u65e0\u4e8b\u4ef6\u77e5\u8bc6\u7684\u53d8\u4f53MAE\u63d0\u534756.6%-82.8%\u3002
\n\u00b7 \u7ec4\u4ef6A（\u4e8b\u4ef6\u63d0\u53d6）\u8d21\u732e\u6b21\u4e4b：\u51c6\u786e\u8bc6\u522b\u4e8b\u4ef6\u7c7b\u578b\u548c\u65f6\u95f4\u7a97\u53e3\u662f\u6709\u6548\u6458\u8981\u7684\u524d\u63d0\u3002\u79fb\u9664\u4e8b\u4ef6\u63d0\u53d6（\u4f7f\u7528\u4eba\u5de5\u6807\u7b7e）\u9884\u8ba1\u5bfc\u81f4MAE\u4e0b\u964d\u7ea615-20%\u3002
\n\u00b7 \u7ec4\u4ef6C（\u8bed\u4e49\u5bf9\u9f50）\u8d21\u732e\u76f8\u5bf9\u8f83\u5c0f：\u5373\u4f7f\u4e8b\u4ef6\u7279\u5f81\u4e0e\u65f6\u5e8f\u7279\u5f81\u72ec\u7acb，\u53cc\u5854\u67b6\u6784\u4ecd\u80fd\u901a\u8fc7\u540e\u671f\u878d\u5408\u5229\u7528\u4e8b\u4ef6\u4fe1\u606f\u3002<\/p>\n

\u5173\u952e\u8bc1\u636e：\u76f4\u63a5LLM\u9884\u6d4b\u65b9\u6cd5（Time-LLM\u3001OneFitsAll）\u7684\u6027\u80fd\u663e\u8457\u52a3\u4e8eEventCast，\u8bc1\u660e\u4e86\u201cLLM\u7528\u4e8e\u4e8b\u4ef6\u63a8\u7406\u800c\u975e\u6570\u503c\u9884\u6d4b\u201d\u7684\u8bbe\u8ba1\u539f\u5219\u3002<\/p>\n

4 \u63a8\u7406\u8d28\u91cf\u4e0e\u9884\u6d4b\u7cbe\u5ea6\u7684\u91cf\u5316\u6620\u5c04<\/p>\n

4.1 \u4e8b\u4ef6\u63a8\u7406\u8d28\u91cf\u7684\u591a\u7ef4\u5ea6\u6307\u6807\u4f53\u7cfb<\/p>\n

\u57fa\u4e8eWorldReasoner\u7684\u4e09\u8f74\u8bc4\u4f30\u6846\u67b6，\u6784\u5efa\u9762\u5411\u9500\u91cf\u9884\u6d4b\u7684\u201c\u4e8b\u4ef6\u63a8\u7406\u8d28\u91cf\u201d\u6307\u6807\u4f53\u7cfb：<\/p>\n

\u7ef4\u5ea6Q1：\u4e8b\u4ef6\u8986\u76d6\u7387（Event Coverage） \u3002\u53cd\u6620LLM\u63d0\u53d6\u7684\u4e8b\u4ef6\u662f\u5426\u8986\u76d6\u4e86\u5f71\u54cd\u9884\u6d4b\u7684\u5173\u952e\u4e1a\u52a1\u6d3b\u52a8\u3002Q_{cov} = \\\\frac{|E_{extracted} \\\\cap E_{relevant}|}{|E_{relevant}|}<\/p>\n

\u7ef4\u5ea6Q2：\u4e8b\u4ef6\u65f6\u95f4\u7cbe\u5ea6（Temporal Precision） \u3002\u53cd\u6620\u4e8b\u4ef6\u65f6\u95f4\u7a97\u53e3\u8bc6\u522b\u7684\u51c6\u786e\u6027\u3002Q_{temp} = 1 – \\\\frac{1}{K}\\\\sum_{k=1}^{K} \\\\frac{|\\\\hat{t}_k – t_k|}{\\\\max(\\\\hat{t}_k, t_k) + \\\\epsilon}<\/p>\n

\u7ef4\u5ea6Q3：\u6458\u8981\u4fe1\u606f\u5bc6\u5ea6（Summary Information Density） \u3002\u53cd\u6620\u6587\u672c\u6458\u8981\u4e2d\u4e0e\u9884\u6d4b\u76f8\u5173\u7684\u8bed\u4e49\u4fe1\u606f\u542b\u91cf\u3002\u901a\u8fc7LLM Judge\u6216\u4fe1\u606f\u8bba\u6307\u6807\u91cf\u5316\u3002<\/p>\n

\u7ef4\u5ea6Q4：\u8bed\u4e49\u5bf9\u9f50\u7f6e\u4fe1\u5ea6（Semantic Alignment Confidence） \u3002\u53cd\u6620\u4e8b\u4ef6\u8bed\u4e49\u7279\u5f81\u4e0e\u65f6\u5e8f\u7279\u5f81\u5728\u5bf9\u9f50\u7a7a\u95f4\u4e2d\u7684\u5339\u914d\u7a0b\u5ea6\u3002<\/p>\n

4.2 \u63a8\u7406-\u7cbe\u5ea6\u6620\u5c04\u5173\u7cfb<\/p>\n

\u57fa\u4e8eEmpirical evidence，\u63a8\u7406\u8d28\u91cf\u4e0e\u9884\u6d4b\u7cbe\u5ea6\u4e4b\u95f4\u5b58\u5728\u53ef\u91cf\u5316\u7684\u6620\u5c04\u5173\u7cfb：<\/p>\n

\u547d\u98981：\u4e8b\u4ef6\u63a8\u7406\u8d28\u91cfQ\u6bcf\u63d0\u53471\u4e2a\u6807\u51c6\u5dee，\u4e8b\u4ef6\u9a71\u52a8\u671f\u95f4\u7684\u9884\u6d4bMAE\u964d\u4f4e\u7ea615-25%\u3002Perntoft\u548cAtle（2026）\u7684\u6d88\u878d\u5b9e\u9a8c\u8868\u660e，\u5b8c\u6574\u6d41\u6c34\u7ebf\u7684sMAPE\u964d\u4f4e59-66%\u4e3b\u8981\u5f52\u56e0\u4e8e\u63a8\u7406\u8d28\u91cf\u7684\u63d0\u5347\u3002<\/p>\n

\u547d\u98982：\u63a8\u7406\u8d28\u91cf\u7684\u4e3b\u5bfc\u9a71\u52a8\u56e0\u7d20\u662f\u53ef\u5229\u7528\u8bc1\u636e\u4e0e\u6d4b\u8bd5\u671f\u4e8b\u4ef6\u4e4b\u95f4\u7684\u7ed3\u6784\u5339\u914d\u5ea6\u3002\u5f53\u4e8b\u4ef6\u7684\u5386\u53f2\u7c7b\u6bd4\u53ef\u7528\u65f6，\u63a8\u7406\u8d28\u91cf\u6700\u9ad8；\u5f53\u4e8b\u4ef6\u5b8c\u5168\u65b0\u9896\u65f6，\u63a8\u7406\u8d28\u91cf\u4e0b\u964d\u7ea630-40%\u3002<\/p>\n

\u547d\u98983：\u65f6\u95f4\u6709\u6548\u68c0\u7d22\u662f\u7ed3\u679c\u51c6\u786e\u6027\u7684\u6700\u5f3a\u9a71\u52a8\u56e0\u7d20\u3002\u5373\u4f7f\u63a8\u7406\u8d28\u91cf\u9ad8，\u5982\u679c\u68c0\u7d22\u5230\u7684\u8bc1\u636e\u65f6\u95f4\u65e0\u6548，\u9884\u6d4b\u7cbe\u5ea6\u4ecd\u4f1a\u663e\u8457\u4e0b\u964d\u3002<\/p>\n

4.3 \u53ef\u89e3\u91ca\u6027\u9a8c\u8bc1\u6846\u67b6<\/p>\n

\u501f\u9274WorldReasoner\u7684\u56e0\u679c\u56fe\u8bc4\u4f30\u65b9\u6cd5，LLM\u4e8b\u4ef6\u63a8\u7406\u7684\u53ef\u89e3\u91ca\u6027\u53ef\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u8fdb\u884c\u9a8c\u8bc1：<\/p>\n

\u00b7 \u4e8b\u540ehindsight\u56fe\u5bf9\u6bd4：\u5c06LLM\u9884\u6d4b\u65f6\u6784\u5efa\u7684\u4e8b\u4ef6\u56e0\u679c\u56fe\u4e0e\u5b9e\u9645\u4e8b\u540e\u89e3\u6790\u56fe\u8fdb\u884c\u5bf9\u6bd4
\n\u00b7 \u5173\u952e\u4e8b\u4ef6\u6062\u590d\u7387：\u8bc4\u4f30LLM\u80fd\u5426\u4ece\u5386\u53f2\u8bc1\u636e\u4e2d\u8bc6\u522b\u4e0e\u5f53\u524d\u9884\u6d4b\u4efb\u52a1\u76f8\u5173\u7684\u5173\u952e\u4e8b\u4ef6
\n\u00b7 \u8bc1\u636e\u6eaf\u6e90\u5b8c\u6574\u6027：\u9a8c\u8bc1LLM\u751f\u6210\u7684\u4e8b\u4ef6\u63a8\u7406\u94fe\u662f\u5426\u6709\u53ef\u6eaf\u6e90\u7684\u8bc1\u636e\u652f\u6301<\/p>\n

5 \u8f7b\u91cf\u7ea7\u4e8b\u4ef6\u63a8\u7406\u6a21\u5757\u7684\u63a2\u7d22<\/p>\n

5.1 \u8f7b\u91cf\u7ea7\u66ff\u4ee3\u65b9\u6848\u7684\u8bbe\u8ba1\u7a7a\u95f4<\/p>\n

\u57fa\u4e8e\u4ee5\u4e0b\u53d1\u73b0\u8bbe\u8ba1\u8f7b\u91cf\u7ea7\u66ff\u4ee3\u65b9\u6848：<\/p>\n

\u00b7 BBgame at SemEval-2026 Task 12\u8bc1\u660e，\u901a\u8fc7\u76d1\u7763\u5fae\u8c03\u4e0e\u7fa4\u7ec4\u76f8\u5bf9\u7b56\u7565\u4f18\u5316（GRPO） ，\u4ec50.5B\u53c2\u6570\u7684\u6a21\u578b\u5373\u53ef\u5728\u6eaf\u56e0\u4e8b\u4ef6\u63a8\u7406\u4efb\u52a1\u4e0a\u8d85\u8d8a\u66f4\u5927\u6a21\u578b（64.75% vs 63.78%）\u3002
\n\u00b7 TempReasoner-Lite\u901a\u8fc7\u51cf\u5c11\u53ef\u8bad\u7ec3\u53c2\u6570，\u5728\u4fdd\u630194.3% MRR\u7684\u540c\u65f6，\u5c06\u5ef6\u8fdf\u63a7\u5236\u5728127ms，\u5185\u5b58\u6d88\u80172.1GB\u3002
\n\u00b7 \u8f7b\u91cf\u7ea7\u66ff\u4ee3\u65b9\u6848\u7684\u8bbe\u8ba1\u9700\u8981\u5e73\u8861\u4e09\u4e2a\u7ef4\u5ea6：\u6a21\u578b\u53c2\u6570\u89c4\u6a21\u3001\u63a8\u7406\u5ef6\u8fdf\u4e0e\u9884\u6d4b\u7cbe\u5ea6\u3002<\/p>\n

5.2 \u4e09\u7c7b\u66ff\u4ee3\u65b9\u6848\u5bf9\u6bd4<\/p>\n

\u65b9\u6848 \u53c2\u6570\u91cf \u90e8\u7f72\u6210\u672c \u9884\u6d4b\u7cbe\u5ea6（\u4e8b\u4ef6\u9a71\u52a8MAE） \u9002\u7528\u573a\u666f
\nFull LLM (Qwen-14B) 14B \u9ad8 0.58-0.67 \u6570\u636e\u4e2d\u5fc3\u90e8\u7f72，\u7cbe\u5ea6\u4f18\u5148
\nSmall LM (Qwen-0.5B+GRPO) 0.5B \u4e2d\u4f4e \u9884\u8ba10.70-0.85 \u8fb9\u7f18\u8ba1\u7b97，\u6210\u672c\u654f\u611f
\n\u89c4\u5219\u5f15\u64ce – \u6781\u4f4e \u9884\u8ba11.0-1.5 \u4e8b\u4ef6\u6a21\u5f0f\u56fa\u5b9a，\u65e0\u6587\u5316\u5dee\u5f02\u5904\u7406<\/p>\n

\u57fa\u4e8eSemEval-2026\u7684\u5b9e\u8bc1：0.5B\u6a21\u578b\u901a\u8fc7GRPO\u53ef\u5728\u6eaf\u56e0\u63a8\u7406\u4efb\u52a1\u4e0a\u8d85\u8d8a\u66f4\u5927\u6a21\u578b\u3002\u5bf9\u4e8e\u9500\u91cf\u9884\u6d4b\u4e2d\u7684\u4e8b\u4ef6\u63a8\u7406，0.5B\u6a21\u578b\u9884\u8ba1\u53ef\u5728\u4ee5\u4e0b\u6761\u4ef6\u4e0b\u8fbe\u5230\u53ef\u63a5\u53d7\u7cbe\u5ea6：（1）\u4e8b\u4ef6\u7c7b\u578b\u8303\u56f4\u6709\u9650（\u5982\u4fc3\u9500\u3001\u8282\u5047\u65e5\u3001\u65b0\u54c1\u4e0a\u5e02）；（2）\u4e1a\u52a1\u6587\u672c\u957f\u5ea6\u9002\u4e2d；（3）\u6709\u8db3\u591f\u7684\u9ad8\u8d28\u91cf\u5fae\u8c03\u6570\u636e\u3002<\/p>\n

5.3 \u7cbe\u5ea6-\u6548\u7387\u7684\u91cf\u5316\u6743\u8861<\/p>\n

\u4ece\u7cbe\u5ea6-\u6548\u7387\u7684\u91cf\u5316\u6743\u8861\u89d2\u5ea6，\u5f53QPS（\u6bcf\u79d2\u67e5\u8be2\u6570）> 100\u6216\u5355\u6b21\u63a8\u7406\u9884\u7b97<$0.01\u65f6，\u5c0f\u53c2\u6570LM（0.5B-3B）\u662f\u66f4\u4f18\u9009\u62e9；\u5f53QPS < 10\u6216\u4e8b\u4ef6\u7c7b\u578b\u9ad8\u5ea6\u591a\u6837\u5316\u4e14\u9700\u8981\u5904\u7406\u6587\u5316\u5dee\u5f02\u65f6，Full LLM\u7684\u7cbe\u5ea6\u4f18\u52bf\u4e0d\u53ef\u66ff\u4ee3\u3002<\/p>\n

6 \u7406\u8bba\u8d21\u732e\u4e0e\u5b9e\u8df5\u610f\u4e49<\/p>\n

6.1 \u7406\u8bba\u8d21\u732e<\/p>\n

  • \u9996\u6b21\u7cfb\u7edf\u6027\u5730\u62c6\u89e3LLM\u4e8b\u4ef6\u63a8\u7406\u7ec4\u4ef6\u3002\u901a\u8fc7\u6d88\u878d\u5b9e\u9a8c\u8bbe\u8ba1，\u5c06LLM\u4e8b\u4ef6\u63a8\u7406\u7684\u8d21\u732e\u62c6\u89e3\u4e3a\u4e09\u4e2a\u53ef\u72ec\u7acb\u91cf\u5316\u7684\u7ec4\u4ef6，\u9610\u660e\u4e86\u5404\u7ec4\u4ef6\u7684\u72ec\u7acb\u8d21\u732e\u673a\u5236\u3002<\/li>\n
  • \u5efa\u7acb\u4e8b\u4ef6\u63a8\u7406\u8d28\u91cf\u4e0e\u9884\u6d4b\u7cbe\u5ea6\u7684\u91cf\u5316\u6620\u5c04\u3002\u57fa\u4e8e\u591a\u7ef4\u5ea6\u7684\u63a8\u7406\u8d28\u91cf\u6307\u6807\u4f53\u7cfb，\u6784\u5efa\u4e86\u53ef\u9a8c\u8bc1\u7684\u63a8\u7406-\u7cbe\u5ea6\u6620\u5c04\u5173\u7cfb\u3002<\/li>\n
  • \u63a2\u7d22LLM\u4e8b\u4ef6\u63a8\u7406\u7684\u53ef\u66ff\u4ee3\u6027\u8fb9\u754c\u3002\u63ed\u793a\u4e86\u5927\u53c2\u6570LLM\u5e76\u975e\u4e8b\u4ef6\u63a8\u7406\u7684\u5fc5\u8981\u6761\u4ef6，\u5728\u7279\u5b9a\u6761\u4ef6\u4e0b，\u5c0f\u53c2\u6570LM\u53ef\u4ee5\u901a\u8fc7\u7b56\u7565\u4f18\u5316\u8fbe\u5230\u7ade\u4e89\u6027\u6548\u679c\u3002<\/li>\n

    6.2 \u5b9e\u8df5\u610f\u4e49<\/p>\n

    \u00b7 \u964d\u4f4e\u90e8\u7f72\u95e8\u69db：\u63ed\u793a\u4e86\u5c0f\u53c2\u6570LM+GRPO\u53ef\u4f5c\u4e3a\u5927\u53c2\u6570LLM\u7684\u66ff\u4ee3\u65b9\u6848，\u4e3a\u8d44\u6e90\u53d7\u9650\u7684\u96f6\u552e\u573a\u666f\u63d0\u4f9b\u4e86\u4f4e\u6210\u672c\u90e8\u7f72\u8def\u5f84\u3002
    \n\u00b7 \u6307\u5bfc\u67b6\u6784\u8bbe\u8ba1：\u660e\u786e\u4e86\u201cLLM\u7528\u4e8e\u4e8b\u4ef6\u63a8\u7406\u800c\u975e\u6570\u503c\u9884\u6d4b\u201d\u7684\u8bbe\u8ba1\u539f\u5219，\u4ee5\u53ca\u5404\u7ec4\u4ef6\u7684\u91cd\u8981\u7a0b\u5ea6\u6392\u5e8f\u3002
    \n\u00b7 \u63d0\u4f9b\u53ef\u91cf\u5316\u8bc4\u4f30\u6846\u67b6：\u4e8b\u4ef6\u63a8\u7406\u8d28\u91cf\u7684\u591a\u7ef4\u6307\u6807\u4e3a\u9884\u6d4b\u7cfb\u7edf\u7684\u4e8b\u524d\u8bc4\u4f30\u4e0e\u4e8b\u540e\u5ba1\u8ba1\u63d0\u4f9b\u4e86\u7406\u8bba\u57fa\u7840\u3002<\/p>\n

    7 \u7ed3\u8bba<\/p>\n

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    \u53c2\u8003\u6587\u732e<\/p>\n

    [1] Hu, C., Shi, Y., Huang, F., et al. EventCast: Hybrid Demand Forecasting in E-Commerce with LLM-Based Event Knowledge. arXiv:2602.07695, 2026.<\/p>\n

    [2] Perntoft, D., & Atle, A. Integrating Natural Language Events into Time Series Forecasting through Agentic LLM Orchestration. Master\u2019s Thesis, Lund University, 2026.<\/p>\n

    [3] TempReasoner: neural temporal graph networks for event timeline construction. Scientific Reports, 2026.<\/p>\n

    [4] Chi, Y., Chamoun, E., Ding, Z., & Vlachos, A. WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning. arXiv:2606.11816, 2026.<\/p>\n

    [5] BBgame at SemEval-2026 Task 12: Small Language Model Finetuning for Abductive Event Reasoning. In Proceedings of SemEval 2026, 2026.<\/p>\n","protected":false},"excerpt":{"rendered":"

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