{"id":7708,"date":"2023-07-23T08:04:46","date_gmt":"2023-07-22T23:04:46","guid":{"rendered":"https:\/\/bocek.co.jp\/media\/?p=7708"},"modified":"2023-07-23T08:04:48","modified_gmt":"2023-07-22T23:04:48","slug":"chatgpt%e3%82%92%e4%bd%bf%e3%81%a3%e3%81%a6%e8%ab%96%e6%96%87%e3%82%92%e8%a6%81%e7%b4%84%e3%81%99%e3%82%8b%e6%96%b9%e6%b3%95%e3%81%be%e3%81%a8%e3%82%81%ef%bc%81%e3%83%97%e3%83%a9%e3%82%b0%e3%82%a4","status":"publish","type":"post","link":"https:\/\/taskhub.jp\/magazine\/exercise\/chatgpt\/7708\/","title":{"rendered":"ChatGPT\u3092\u4f7f\u3063\u3066\u8ad6\u6587\u3092\u8981\u7d04\u3059\u308b\u65b9\u6cd5\u307e\u3068\u3081\uff01\u30d7\u30e9\u30b0\u30a4\u30f3\u3084API\u3092\u6d3b\u7528\u3059\u308b"},"content":{"rendered":"\n

\u300c\u82f1\u8a9e\u306e\u8ad6\u6587\u3092\u8981\u7d04\u3059\u308b\u306e\u304c\u5927\u5909…\u300d\u300cChatGPT\u3092\u4f7f\u3063\u3066\u5b66\u8853\u7684\u306a\u8ad6\u6587\u3092\u52b9\u7387\u826f\u304f\u8981\u7d04\u3067\u304d\u308b\u306e\uff1f…\u300d\u305d\u3046\u601d\u3046\u65b9\u3082\u3044\u308b\u306e\u3067\u306f\u306a\u3044\u3067\u3057\u3087\u3046\u304b\u3002<\/p>\n\n\n\n

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\u4eca\u56dePROMPTY\u3067\u306f\u3001\u30d7\u30e9\u30b0\u30a4\u30f3\u3084API\u3092\u6d3b\u7528\u3057\u305f\u8ad6\u6587\u8981\u7d04\u306e\u65b9\u6cd5\u3092\u4f8b\u3092\u4f7f\u3063\u3066\u3054\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

\u8981\u7d04\u3059\u308b\u8ad6\u6587<\/h2>\n\n\n\n

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\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb\uff1a“Why Should I Trust You?\u201d Explaining the Predictions of Any Classifier<\/a><\/p>\n\n\n\n

\u8ad6\u6587\u306eAbstract\u3092\u305d\u306e\u307e\u307eDeepL\u3067\u548c\u8a33\u3059\u308b\u3068\u6b21\u306b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

\n

ABSTRACT
Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one.<\/p>\n\n\n\n

In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally varound the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.<\/p>\n\n\n\n

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ChatGPT\u306e\u51fa\u529b\u7d50\u679c\uff08AskYourPDF\uff09\uff1a<\/p>\n\n\n\n
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\u30bf\u30a4\u30c8\u30eb\uff08\u548c\u8a33\uff09\n\u300c\u306a\u305c\u3042\u306a\u305f\u3092\u4fe1\u3058\u308b\u3079\u304d\u306a\u306e\u304b\uff1f\u300d\u4efb\u610f\u306e\u5206\u985e\u5668\u306e\u4e88\u6e2c\u3092\u8aac\u660e\u3059\u308b\n\n\u8457\u8005\u3001\u96d1\u8a8c\u540d\u3001\u516c\u958b\u5e74\n\u8457\u8005: Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin\n\u96d1\u8a8c\u540d: KDD 2016 San Francisco, CA, USA\n\u516c\u958b\u5e74: 2016\n\n\u65e5\u672c\u8a9e\u306e\u8981\u7d04\n\n1.\u3053\u306e\u8ad6\u6587\u3067\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u3092\u8aac\u660e\u3059\u308b\u65b0\u3057\u3044\u624b\u6cd5\u3001LIME\uff08Local Interpretable Model-Agnostic Explanations\uff09\u3092\u63d0\u6848\u3057\u3066\u3044\u307e\u3059\u3002LIME\u306f\u3001\u4efb\u610f\u306e\u5206\u985e\u5668\u306e\u4e88\u6e2c\u3092\u89e3\u91c8\u53ef\u80fd\u304b\u3064\u4fe1\u983c\u6027\u306e\u9ad8\u3044\u65b9\u6cd5\u3067\u8aac\u660e\u3057\u307e\u3059\u3002\n2.\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306f\u3001\u591a\u304f\u306e\u5834\u5408\u300c\u30d6\u30e9\u30c3\u30af\u30dc\u30c3\u30af\u30b9\u300d\u306e\u3088\u3046\u306b\u632f\u308b\u821e\u3044\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u4e88\u6e2c\u306e\u80cc\u5f8c\u306b\u3042\u308b\u7406\u7531\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u306f\u3001\u30e2\u30c7\u30eb\u3092\u4fe1\u983c\u3057\u3001\u65b0\u3057\u3044\u30e2\u30c7\u30eb\u306e\u5c0e\u5165\u3092\u6c7a\u5b9a\u3059\u308b\u4e0a\u3067\u91cd\u8981\u3067\u3059\u3002\n3.LIME\u306f\u3001\u4e88\u6e2c\u306e\u8fd1\u304f\u3067\u89e3\u91c8\u53ef\u80fd\u306a\u30e2\u30c7\u30eb\u3092\u5b66\u7fd2\u3059\u308b\u3053\u3068\u3067\u3001\u4efb\u610f\u306e\u5206\u985e\u5668\u306e\u4e88\u6e2c\u3092\u8aac\u660e\u3057\u307e\u3059\u3002\u307e\u305f\u3001\u30e2\u30c7\u30eb\u3092\u8aac\u660e\u3059\u308b\u305f\u3081\u306e\u65b9\u6cd5\u3068\u3057\u3066\u3001\u4ee3\u8868\u7684\u306a\u500b\u3005\u306e\u4e88\u6e2c\u3068\u305d\u306e\u8aac\u660e\u3092\u975e\u5197\u9577\u306a\u65b9\u6cd5\u3067\u63d0\u793a\u3059\u308b\u65b9\u6cd5\u3092\u63d0\u6848\u3057\u3066\u3044\u307e\u3059\u3002\n4.LIME\u306e\u6709\u7528\u6027\u306f\u3001\u30c6\u30ad\u30b9\u30c8\uff08\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u306a\u3069\uff09\u3084\u753b\u50cf\u5206\u985e\uff08\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306a\u3069\uff09\u306e\u3055\u307e\u3056\u307e\u306a\u30e2\u30c7\u30eb\u3092\u8aac\u660e\u3059\u308b\u3053\u3068\u3067\u793a\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u307e\u305f\u3001\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3068\u4eba\u9593\u306e\u88ab\u9a13\u8005\u3092\u7528\u3044\u305f\u65b0\u305f\u306a\u5b9f\u9a13\u3092\u901a\u3058\u3066\u3001\u4e88\u6e2c\u3092\u4fe1\u983c\u3059\u308b\u305f\u3081\u306b\u5fc5\u8981\u306a\u30b7\u30ca\u30ea\u30aa\u3067\u306e\u8aac\u660e\u306e\u6709\u7528\u6027\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/code><\/pre>\n\n\n\n
\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u6307\u5b9a\u3057\u305f\u51fa\u529b\u5f62\u5f0f\u306b\u5247\u3063\u3066\u3001\u8ad6\u6587\u306e\u60c5\u5831\u3068\u8981\u7d04\u304c\u51fa\u529b\u3055\u308c\u307e\u3057\u305f\u3002\u30bf\u30a4\u30c8\u30eb\uff08\u548c\u8a33\uff09\u3084\u8457\u8005\u30fb\u96d1\u8a8c\u540d\u30fb\u516c\u958b\u5e74\u306e\u60c5\u5831\u3082\u6f0f\u308c\u306a\u304f\u62fe\u3063\u3066\u304f\u308c\u3066\u3044\u307e\u3059\u3002
\u8981\u7d04\u306e\u6587\u7ae0\u306f\u5c11\u3057\u62bd\u8c61\u7684\u306a\u5185\u5bb9\u306b\u306a\u3063\u3066\u3057\u307e\u3044\u307e\u3057\u305f\u304c\u30011\u3064\u76ee\u306e\u7b87\u6761\u66f8\u304d\u3067\u8ad6\u6587\u306e\u4e3b\u984c\u3092\u7aef\u7684\u306b\u8ff0\u3079\u30012\u3064\u76ee\u30013\u3064\u76ee\u306e\u7b87\u6761\u66f8\u304d\u3067\u7814\u7a76\u306e\u610f\u7fa9\u3084\u7279\u5fb4\u3092\u8aac\u660e\u3057\u3066\u30014\u3064\u76ee\u306e\u7b87\u6761\u66f8\u304d\u3067\u306f\u3001\u7814\u7a76\u306e\u6709\u7528\u6027\u306b\u89e6\u308c\u3066\u304a\u308a\u3001\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u624b\u9806\u901a\u308a\u306b\u8981\u7d04\u3067\u304d\u3066\u3044\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
AskYourPDF\u30d7\u30e9\u30b0\u30a4\u30f3\u306e\u305d\u306e\u4ed6\u306e\u6d3b\u7528\u65b9\u6cd5\u306b\u3064\u3044\u3066\u306f\u3053\u3061\u3089\u306e\u8a18\u4e8b\u3067\u8a73\u3057\u304f\u89e3\u8aac\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n\n\n\n
\u2461LinkReader\u30d7\u30e9\u30b0\u30a4\u30f3\u3092\u6d3b\u7528\u3059\u308b<\/h3>\n\n\n\n
\u6b21\u306b\u3001\u30d7\u30e9\u30b0\u30a4\u30f3\u3092\u4f7f\u3063\u3066\u8ad6\u6587\u3092\u8981\u7d04\u3059\u308b\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002LinkReader\u30d7\u30e9\u30b0\u30a4\u30f3\u306f\u3001\u30a6\u30a7\u30d6\u30da\u30fc\u30b8\u3001PDF\u3001PPT\u3001\u753b\u50cf\u3001\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3001Google\u30c9\u30e9\u30a4\u30d6\u3001YouTube\u30ea\u30f3\u30af\u306a\u3069\u3001\u3042\u3089\u3086\u308b\u7a2e\u985e\u306eURL\u304b\u3089\u30b3\u30f3\u30c6\u30f3\u30c4\u3092\u53d6\u5f97\u3067\u304d\u308b\u3053\u3068\u304c\u5927\u304d\u306a\u7279\u5fb4\u3067\u3059\u3002<\/p>\n\n\n\n
\u30d7\u30ed\u30f3\u30d7\u30c8\u306e#\u8ad6\u6587\u306e\u30ea\u30f3\u30af\u306b\u306fWeb \u30da\u30fc\u30b8\u306e\u30ea\u30f3\u30af\u3092\u8cbc\u308a\u4ed8\u3051\u307e\u3059\u3002<\/p>\n\n\n\n
ChatGPT\u306e\u51fa\u529b\uff08LinkReader\u30d7\u30e9\u30b0\u30a4\u30f3\uff09\uff1a<\/p>\n\n\n\n
\u3053\u306e\u8ad6\u6587\u306e\u30bf\u30a4\u30c8\u30eb\u306f\u300c\u201cWhy Should I Trust You?\u201d Explaining the Predictions of Any Classifier\u300d\u3067\u3001\u8457\u8005\u306fMarco Tulio Ribeiro\u3001Sameer Singh\u3001Carlos Guestrin\u3067\u3001\u30ef\u30b7\u30f3\u30c8\u30f3\u5927\u5b66\u306b\u6240\u5c5e\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u306e\u8ad6\u6587\u306f2016\u5e74\u306b\u516c\u958b\u3055\u308c\u307e\u3057\u305f\u3002\n\n\u4ee5\u4e0b\u306b\u3001\u3053\u306e\u8ad6\u6587\u306e\u8981\u7d04\u3092\u793a\u3057\u307e\u3059\u3002\n\n1.\u3053\u306e\u7814\u7a76\u3067\u306f\u3001LIME\uff08Local Interpretable Model-Agnostic Explanations\uff09\u3068\u3044\u3046\u65b0\u3057\u3044\u8aac\u660e\u624b\u6cd5\u3092\u63d0\u6848\u3057\u3066\u3044\u307e\u3059\u3002LIME\u306f\u3001\u4efb\u610f\u306e\u5206\u985e\u5668\u306e\u4e88\u6e2c\u3092\u89e3\u91c8\u53ef\u80fd\u304b\u3064\u4fe1\u983c\u6027\u306e\u3042\u308b\u65b9\u6cd5\u3067\u8aac\u660e\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u304c\u300c\u30d6\u30e9\u30c3\u30af\u30dc\u30c3\u30af\u30b9\u300d\u3067\u3042\u308b\u554f\u984c\u3092\u89e3\u6c7a\u3057\u3001\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u306e\u80cc\u5f8c\u306b\u3042\u308b\u7406\u7531\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002\n\n2.LIME\u306f\u3001\u4e88\u6e2c\u306e\u8fd1\u508d\u3067\u89e3\u91c8\u53ef\u80fd\u306a\u30e2\u30c7\u30eb\u3092\u5b66\u7fd2\u3059\u308b\u3053\u3068\u3067\u3001\u4efb\u610f\u306e\u5206\u985e\u5668\u306e\u4e88\u6e2c\u3092\u8aac\u660e\u3057\u307e\u3059\u3002\u307e\u305f\u3001\u500b\u3005\u306e\u4e88\u6e2c\u3068\u305d\u306e\u8aac\u660e\u3092\u975e\u5197\u9577\u306a\u65b9\u6cd5\u3067\u63d0\u793a\u3059\u308b\u30e2\u30c7\u30eb\u306e\u8aac\u660e\u65b9\u6cd5\u3082\u63d0\u6848\u3057\u3066\u3044\u307e\u3059\u3002\n\n3.\u3053\u306e\u7814\u7a76\u3067\u306f\u3001\u30c6\u30ad\u30b9\u30c8\uff08\u4f8b\u3048\u3070\u3001\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\uff09\u3084\u753b\u50cf\u5206\u985e\uff08\u4f8b\u3048\u3070\u3001\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff09\u306a\u3069\u3001\u3055\u307e\u3056\u307e\u306a\u30e2\u30c7\u30eb\u306e\u8aac\u660e\u3092\u884c\u3046\u3053\u308c\u3089\u306e\u65b9\u6cd5\u306e\u67d4\u8edf\u6027\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002\n\n4.LIME\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u30e6\u30fc\u30b6\u30fc\u306f\u4e88\u6e2c\u3092\u4fe1\u983c\u3059\u3079\u304d\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3057\u305f\u308a\u3001\u30e2\u30c7\u30eb\u9593\u3067\u9078\u629e\u3057\u305f\u308a\u3001\u4fe1\u983c\u6027\u306e\u4f4e\u3044\u5206\u985e\u5668\u3092\u6539\u5584\u3057\u305f\u308a\u3001\u5206\u985e\u5668\u3092\u4fe1\u983c\u3059\u3079\u304d\u3067\u306a\u3044\u7406\u7531\u3092\u7279\u5b9a\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002\n\n\u4ee5\u4e0a\u306e\u8981\u7d04\u306f\u3001\u8ad6\u6587\u306e\u5404\u7ae0\u306e\u5185\u5bb9\u3092\u8981\u7d04\u3057\u3001\u305d\u308c\u3089\u306e\u95a2\u9023\u6027\u3092\u8003\u616e\u306b\u5165\u308c\u3066\u8ad6\u6587\u5168\u4f53\u3092\u8981\u7d04\u3057\u305f\u3082\u306e\u3067\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u7814\u7a76\u306e\u554f\u3044\u3084\u8ad6\u6587\u306e\u65b0\u898f\u6027\u3001\u7814\u7a76\u7d50\u679c\u306e\u610f\u5473\u3084\u5f71\u97ff\u304c\u660e\u78ba\u306b\u7406\u89e3\u3067\u304d\u307e\u3059\u3002<\/code><\/pre>\n\n\n\n
\u30bf\u30a4\u30c8\u30eb\u306e\u548c\u8a33\u306f\u51fa\u3066\u304d\u307e\u305b\u3093\u3067\u3057\u305f\u304c\u3001\u6982\u306d\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u6307\u793a\u901a\u308a\u306b\u8ad6\u6587\u3092\u8981\u7d04\u3057\u3066\u304f\u308c\u307e\u3057\u305f\u3002
\u8981\u7d04\u306e\u5185\u5bb9\u306fAskYourPDF\u3067\u8981\u7d04\u3057\u305f\u5834\u5408\u3068\u5927\u304d\u306a\u5dee\u306f\u3042\u308a\u307e\u305b\u3093\u3067\u3057\u305f\u3002<\/p>\n\n\n\n
LinkReader\u30d7\u30e9\u30b0\u30a4\u30f3\u306e\u305d\u306e\u4ed6\u306e\u6d3b\u7528\u65b9\u6cd5\u306b\u3064\u3044\u3066\u306f\u3053\u3061\u3089\u306e\u8a18\u4e8b\u3067\u8a73\u3057\u304f\u89e3\u8aac\u3057\u3066\u3044\u307e\u3059<\/p>\n\n\n\n\n\n\n
\u2462ScholarAI\u30d7\u30e9\u30b0\u30a4\u30f3\u3092\u6d3b\u7528\u3059\u308b<\/h3>\n\n\n\n
\u6b21\u306b\u3001ScholarAI\u30d7\u30e9\u30b0\u30a4\u30f3\u3092\u4f7f\u3063\u3066\u8ad6\u6587\u3092\u8981\u7d04\u3059\u308b\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002ScholarAI\u30d7\u30e9\u30b0\u30a4\u30f3\u306f\u3001\u79d1\u5b66\u7684\u306a\u6587\u732e\u3092\u691c\u7d22\u30fb\u62bd\u51fa\u3067\u304d\u308b\u30d7\u30e9\u30b0\u30a4\u30f3\u3067\u3059\u3002\u30ad\u30fc\u30ef\u30fc\u30c9\u306b\u57fa\u3065\u3044\u3066\u95a2\u9023\u3059\u308b\u8ad6\u6587\u306e\u6284\u9332\u3092\u691c\u7d22\u3057\u3001\u5168\u6587\u3092\u53d6\u5f97\u3067\u304d\u308b\u3053\u3068\u304c\u7279\u9577\u3067\u3059\u3002<\/p>\n\n\n\n
\u30d7\u30ed\u30f3\u30d7\u30c8\u306e#\u8ad6\u6587\u306e\u30ea\u30f3\u30af\u306b\u306fWeb \u30da\u30fc\u30b8\u306e\u30ea\u30f3\u30af\u3092\u8cbc\u308a\u4ed8\u3051\u307e\u3059\u3002<\/p>\n\n\n\n
ChatGPT\u306e\u51fa\u529b\uff08ScholarAI\u30d7\u30e9\u30b0\u30a4\u30f3\uff09\uff1a<\/p>\n\n\n\n
\u3053\u306e\u8ad6\u6587\u306e\u8981\u7d04\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\uff1a\n\n1.\u3053\u306e\u8ad6\u6587\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u3092\u8aac\u660e\u3059\u308b\u305f\u3081\u306e\u65b0\u3057\u3044\u624b\u6cd5\u3001LIME\uff08Local Interpretable Model-Agnostic Explanations\uff09\u3092\u63d0\u6848\u3057\u3066\u3044\u307e\u3059\u3002LIME\u306f\u3001\u4efb\u610f\u306e\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u3092\u3001\u4eba\u9593\u304c\u7406\u89e3\u53ef\u80fd\u306a\u5f62\u3067\u8aac\u660e\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\n\n2.LIME\u306f\u3001\u4e88\u6e2c\u306e\u5c40\u6240\u7684\u306a\u4fe1\u983c\u6027\u3092\u8a55\u4fa1\u3059\u308b\u305f\u3081\u306b\u3001\u7279\u5b9a\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u5468\u8fba\u306e\u30c7\u30fc\u30bf\u3092\u751f\u6210\u3057\u3001\u305d\u308c\u3092\u7528\u3044\u3066\u30e2\u30c7\u30eb\u306e\u6319\u52d5\u3092\u8fd1\u4f3c\u3057\u307e\u3059\u3002\u3053\u306e\u624b\u6cd5\u306f\u3001\u30e2\u30c7\u30eb\u5168\u4f53\u3092\u7406\u89e3\u3059\u308b\u306e\u3067\u306f\u306a\u304f\u3001\u500b\u3005\u306e\u4e88\u6e2c\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u306b\u7126\u70b9\u3092\u5f53\u3066\u3066\u3044\u307e\u3059\u3002\n\n3.\u8457\u8005\u3089\u306f\u307e\u305f\u3001SP-LIME\u3068\u3044\u3046\u624b\u6cd5\u3082\u63d0\u6848\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u30e2\u30c7\u30eb\u5168\u4f53\u306e\u6319\u52d5\u3092\u7406\u89e3\u3059\u308b\u305f\u3081\u306b\u3001\u4ee3\u8868\u7684\u3067\u975e\u5197\u9577\u306a\u4e88\u6e2c\u3092\u9078\u629e\u3059\u308b\u65b9\u6cd5\u3067\u3059\u3002\n\n4.\u5b9f\u9a13\u7d50\u679c\u306f\u3001LIME\u3068SP-LIME\u304c\u3001\u30e2\u30c7\u30eb\u9593\u306e\u9078\u629e\u3001\u4fe1\u983c\u6027\u306e\u8a55\u4fa1\u3001\u4e0d\u4fe1\u983c\u306a\u30e2\u30c7\u30eb\u306e\u6539\u5584\u3001\u4e88\u6e2c\u306e\u6d1e\u5bdf\u306e\u63d0\u4f9b\u3068\u3044\u3063\u305f\u3001\u30c6\u30ad\u30b9\u30c8\u3068\u753b\u50cf\u306e\u9818\u57df\u3067\u306e\u4fe1\u983c\u95a2\u9023\u30bf\u30b9\u30af\u306b\u304a\u3044\u3066\u6709\u7528\u3067\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002\n\n\u4ee5\u4e0b\u306f\u3001\u65e5\u672c\u8a9e\u3067\u306e\u51fa\u529b\u3067\u3059\uff1a\n\n*\u30bf\u30a4\u30c8\u30eb\uff1a\u300c\"Why Should I Trust You?\" Explaining the Predictions of Any Classifier\u300d\uff08\u300c\u306a\u305c\u3042\u306a\u305f\u3092\u4fe1\u3058\u308b\u3079\u304d\u306a\u306e\u304b\uff1f \u4efb\u610f\u306e\u5206\u985e\u5668\u306e\u4e88\u6e2c\u3092\u8aac\u660e\u3059\u308b\u300d\uff09\n*\u8457\u8005\uff1aMarco Tulio Ribeiro, Sameer Singh, Carlos Guestrin\n*\u96d1\u8a8c\u540d\uff1aProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining\n*\u516c\u958b\u5e74\uff1a2016\n\n*\u65e5\u672c\u8a9e\u306e\u8981\u7d04\uff1a\n\n\u672c\u8ad6\u6587\u3067\u306f\u3001LIME\uff08Local Interpretable Model-Agnostic Explanations\uff09\u3068\u3044\u3046\u65b0\u3057\u3044\u624b\u6cd5\u3092\u63d0\u6848\u3057\u3066\u3044\u307e\u3059\u3002LIME\u306f\u3001\u4efb\u610f\u306e\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u3092\u3001\u4eba\u9593\u304c\u7406\u89e3\u53ef\u80fd\u306a\u5f62\u3067\u8aac\u660e\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\nLIME\u306f\u3001\u7279\u5b9a\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u5468\u8fba\u306e\u30c7\u30fc\u30bf\u3092\u751f\u6210\u3057\u3001\u305d\u308c\u3092\u7528\u3044\u3066\u30e2\u30c7\u30eb\u306e\u6319\u52d5\u3092\u8fd1\u4f3c\u3059\u308b\u3053\u3068\u3067\u3001\u4e88\u6e2c\u306e\u5c40\u6240\u7684\u306a\u4fe1\u983c\u6027\u3092\u8a55\u4fa1\u3057\u307e\u3059\u3002\u3053\u306e\u624b\u6cd5\u306f\u3001\u30e2\u30c7\u30eb\u5168\u4f53\u3092\u7406\u89e3\u3059\u308b\u306e\u3067\u306f\u306a\u304f\u3001\u500b\u3005\u306e\u4e88\u6e2c\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u306b\u7126\u70b9\u3092\u5f53\u3066\u3066\u3044\u307e\u3059\u3002\n\u3055\u3089\u306b\u3001\u8457\u8005\u3089\u306fSP-LIME\u3068\u3044\u3046\u624b\u6cd5\u3082\u63d0\u6848\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u30e2\u30c7\u30eb\u5168\u4f53\u306e\u6319\u52d5\u3092\u7406\u89e3\u3059\u308b\u305f\u3081\u306b\u3001\u4ee3\u8868\u7684\u3067\u975e\u5197\u9577\u306a\u4e88\u6e2c\u3092\u9078\u629e\u3059\u308b\u65b9\u6cd5\u3067\u3059\u3002\n\u5b9f\u9a13\u7d50\u679c\u306f\u3001LIME\u3068SP-LIME\u304c\u3001\u30e2\u30c7\u30eb\u9593\u306e\u9078\u629e\u3001\u4fe1\u983c\u6027\u306e\u8a55\u4fa1\u3001\u4e0d\u4fe1\u983c\u306a\u30e2\u30c7\u30eb\u306e\u6539\u5584\u3001\u4e88\u6e2c\u306e\u6d1e\u5bdf\u306e\u63d0\u4f9b\u3068\u3044\u3063\u305f\u3001\u30c6\u30ad\u30b9\u30c8\u3068\u753b\u50cf\u306e\u9818\u57df\u3067\u306e\u4fe1\u983c\u95a2\u9023\u30bf\u30b9\u30af\u306b\u304a\u3044\u3066\u6709\u7528\u3067\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/code><\/pre>\n\n\n\n
2\u56de\u5206\u306e\u8981\u7d04\u304c\u51fa\u529b\u3055\u308c\u3066\u3057\u307e\u3057\u307e\u3057\u305f\u304c\u3001\u305d\u308c\u4ee5\u5916\u306f\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u6307\u793a\u3057\u305f\u901a\u308a\u306e\u5f62\u5f0f\u3067\u8ad6\u6587\u304c\u8981\u7d04\u3055\u308c\u307e\u3057\u305f\u3002\u5185\u5bb9\u306fLinkReader\u3068AskYourPDF\u3092\u4f7f\u3063\u305f\u5834\u5408\u3068\u7570\u306a\u308a\u3001\u8ad6\u6587\u306e\u5f8c\u534a\u3067\u767b\u5834\u3059\u308bSP-LIME\u3068\u3044\u3046\u624b\u6cd5\u304c\u53d6\u308a\u4e0a\u3052\u3089\u308c\u3066\u3044\u307e\u3059\u3002\u3053\u308c\u306f\u3001ScholarAI\u30d7\u30e9\u30b0\u30a4\u30f3\u3067\u306f\u8ad6\u6587\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u5168\u4f53\u3092\u51e6\u7406\u3067\u304d\u3066\u3044\u308b\u3053\u3068\u3092\u793a\u3057\u3001\u5185\u5bb9\u306e\u5145\u5b9f\u5ea6\u304c\u9ad8\u307e\u3063\u3066\u3044\u307e\u3059\u3002\u305f\u3060\u3001\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u6307\u793a\u3057\u305f\u3088\u3046\u306a\u7814\u7a76\u306e\u610f\u7fa9\u3084\u5f71\u97ff\u306b\u3064\u3044\u3066\u306f\u5c11\u3057\u8aad\u307f\u53d6\u308a\u306b\u304f\u3044\u6587\u7ae0\u306b\u306a\u3063\u3066\u3044\u308b\u5370\u8c61\u3092\u53d7\u3051\u307e\u3059\u3002<\/p>\n\n\n\n
\u4eca\u56de\u306e\u8981\u7d04\u306e\u7d50\u679c\u304b\u3089\u30013\u3064\u306e\u30d7\u30e9\u30b0\u30a4\u30f3\u3092\u6bd4\u8f03\u3059\u308b\u3068\u3001\u5185\u5bb9\u306e\u5145\u5b9f\u5ea6\u306fScholarAI>AskYourPDF=LinkReader\u3001\u30d7\u30ed\u30f3\u30d7\u30c8\u3078\u306e\u5fe0\u5b9f\u6027\u3067\u306f\u3001AskYourPDF>LinkReader>ScholarAI\u3068\u3044\u3046\u7d50\u679c\u306b\u306a\u308a\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
\u3053\u3061\u3089\u3092\u53c2\u8003\u306b\u3057\u3066\u3001\u8ad6\u6587\u3092\u306f\u3058\u3081\u3068\u3057\u305f\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306e\u8981\u7d04\u306b\u4f7f\u3046\u30d7\u30e9\u30b0\u30a4\u30f3\u3092\u9078\u629e\u3057\u3066\u3044\u305f\u3060\u3051\u308c\u3070\u5e78\u3044\u3067\u3059\u3002<\/p>\n\n\n\n
OpenAI\u306eAPI\u3092\u4f7f\u3063\u3066\u8ad6\u6587\u3092\u8981\u7d04\u3059\u308b\u65b9\u6cd5<\/h2>\n\n\n\n
OpenAI\u306eAPI\u3092\u4f7f\u3046\u3053\u3068\u3067\u3001\u8ad6\u6587\u3092\u8981\u7d04\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
ChatGPT\u306e\u30d7\u30e9\u30b0\u30a4\u30f3\u3092\u8d77\u52d5\u3057\u3066\u3001\u305d\u306e\u90fd\u5ea6\u30d7\u30ed\u30f3\u30d7\u30c8\u3092\u5165\u529b\u3057\u306a\u304f\u3066\u3082\u3088\u3044\u305f\u3081\u3001\u51e6\u7406\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u308b\u305f\u3081\u3001\u52b9\u7387\u7684\u306b\u8ad6\u6587\u3092\u8981\u7d04\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001\u51fa\u529b\u5f62\u5f0f\u306a\u3069\u3092\u81ea\u7531\u306b\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3059\u3002<\/p>\n\n\n\n
\u4eca\u56de\u306f\u3001Python\u3067\u6b21\u306e\u3088\u3046\u306a\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u3067\u8ad6\u6587\u3092\u8981\u7d04\u3057\u307e\u3059\u3002\u30e2\u30c7\u30eb\u306ftext-davinci-003\uff08GPT-3\uff09\u306e\u305f\u3081\u3001ChatGPT\u30d7\u30e9\u30b0\u30a4\u30f3\u3067\u4f7f\u308f\u308c\u308bGPT-4\u3088\u308a\u3082\u52a3\u3063\u305f\u30e2\u30c7\u30eb\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
# \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\nimport openai\nfrom PyPDF2 import PdfReader\n\n# OpenAI\u306eAPI\u30ad\u30fc\u3092\u8a2d\u5b9a\nopenai.api_key = {API\u30ad\u30fc\u3092\u5165\u529b}<\/mark><\/strong>\n\ndef summarize_pdf(file_path):\n    # PDF\u3092\u8aad\u307f\u8fbc\u3080\u3002\n    with open(file_path, 'rb') as file:\n        reader = PdfReader(file)\n        text = \"\"\n        for page in reader.pages:\n            text += page.extract_text()\n\n    # \u8ad6\u6587\u3092\u8981\u7d04\u3059\u308b\u305f\u3081\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u3092\u4f5c\u6210\u3002\n    prompt = f\"\\\n        #\u547d\u4ee4\u6587\u3042\u306a\u305f\u306f\u30d7\u30ed\u306e\u7ffb\u8a33\u8005\u3067\u3059\u3002\\\n        #\u624b\u9806\u306b\u5f93\u3063\u3066\u8ad6\u6587\u3092\u8981\u7d04\u3057\u3066\u304f\u3060\u3055\u3044\u3002\\\n        #\u51fa\u529b\u5f62\u5f0f\u306b\u5f93\u3046\u3053\u3068\\\n        #\u624b\u9806\\\n        1.\u8ad6\u6587\u306e\u30ea\u30f3\u30af\u3092\u8aad\u307f\u8fbc\u3080\\\n        2.\u8ad6\u6587\u306e\u5404\u7ae0\u306e\u5185\u5bb9\u3092\u8981\u7d04\u3059\u308b\\\n        3.\u5404\u7ae0\u306e\u7e4b\u304c\u308a\u3092\u95a2\u9023\u4ed8\u3051\u3066\u3001\u8ad6\u6587\u5168\u4f53\u3092\u8981\u7d04\u3059\u308b\u3053\u3068\\\n        4.\u8ad6\u6587\u306e\u8981\u7d04\u30923~5\u500b\u306e\u7b87\u6761\u66f8\u304d\u3067\u793a\u3059\u3002\u4e00\u3064\u306e\u7b87\u6761\u66f8\u304d\u306f100\u5b57\u7a0b\u5ea6\u306e\u6587\u7ae0\u306b\u3059\u308b\u3053\u3068\u3002\u7814\u7a76\u306e\u554f\u3044\u3084\u3001\u8ad6\u6587\u306e\u65b0\u898f\u6027\u3001\u7814\u7a76\u7d50\u679c\u306e\u610f\u5473\u3084\u5f71\u97ff\u304c\u3081\u3044\u304b\u304f\u306b\u308f\u304b\u308b\u3088\u3046\u306b\u307e\u3068\u3081\u308b\u3053\u3068\\\n        5.\u8ad6\u6587\u306e\u8981\u7d04\u3092\u81ea\u7136\u306a\u65e5\u672c\u8a9e\u306b\u8a33\u3057\u3066\u51fa\u529b\u3059\u308b\u3053\u3068\\\n        #\u51fa\u529b\u5f62\u5f0f*\u30bf\u30a4\u30c8\u30eb\uff08\u548c\u8a33\uff09*\u8457\u8005\u3001\u96d1\u8a8c\u540d\u3001\u516c\u958b\u5e74*\u65e5\u672c\u8a9e\u306e\u8981\u7d04\uff1c\u7b87\u6761\u66f8\u304d\u3067\u793a\u3059\uff1e{text[:5000]}\"\n\n    # \u30e2\u30c7\u30eb\u3092\u8a2d\u5b9a\u3059\u308b\u3002\u8981\u7d04\u6587\u306e\u30c8\u30fc\u30af\u30f3\u6570\u3092\u6307\u5b9a\u3059\u308b\u3002\n    response = openai.Completion.create(\n        engine=\"text-davinci-003\",\n        prompt=prompt,\n        temperature=0.3,\n        max_tokens=2000\n    )\n    return response.choices[0].text.strip()\n\n#PDF\u3092\u8981\u7d04\u3059\u308b\u3002\nsummary = summarize_pdf(\"Why Should I Trust You Explaining the Predictions of Any Classifier.pdf\")\nprint('- ' + summary)<\/code><\/pre>\n\n\n\n
\u5b9f\u884c\u7d50\u679c\uff1a<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
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2023\u5e747\u67086\u65e5\u306b\u306f\u3001GPT-4\u306eAPI\u304c\u6709\u6599\u30e6\u30fc\u30b6\u30fc\u5411\u3051\u306b\u516c\u958b\u3055\u308c\u3066\u3044\u308b\u305f\u3081\u3001OpenAI\u306eAPI\u3092\u4f7f\u3063\u3066\u8ad6\u6587\u8981\u7d04\u3092\u59cb\u3081\u3068\u3057\u305f\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306e\u51e6\u7406\u306e\u30af\u30aa\u30ea\u30c6\u30a3\u3082\u4e0a\u304c\u308b\u3068\u8003\u3048\u3089\u308c\u307e\u3059\u3002<\/p>\n\n\n\n
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    \n
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