If the text is a TGM-generated sentence, we expect the distribution of predicted next tokens to be more relevant to the actual next tokens of the given text. This article introduced the current state of text generation models (TGM) and automatic text detection techniques. The upper panel shows the human text and the lower panel shows the generated text from GPT-2 large (temperature=0.7). 擁護できないもんねぇ、困ったねぇw, こういう派手なやけくそ神話レア本当好き (e.g., to specify the topic of the article to be generated.). As with zero-shot, the model used for text generation and the model used for auto-generated sentence detection may be the same or different. We restrict the tokens to be sampled to those with a predicted probability greater than or equal to the threshold $p$. In this setting, no supervised data is needed for the automatic sentence detection. It was also shown that detectors trained on large GPT-2 were able to adequately detect small-scale GPT-2-generated sentences. We will introduce some of the main research examples of such detectors. 根本原理と同じマナ域、同等の色拘束で手札と墓地入れ替えるだけなのは実際弱いわ This study tested how the detection accuracy varies for different numbers of parameters (117M, 345M, 762M, and 1542M) and sampling techniques (pure sampling, top-k sampling, and top-p sampling). Unrestricted sampling samples tokens directly from the predicted probability distribution. ・新ルーカが伝説の狐と両面になって登場、毎ターン墓地から呪文を使う伝説の鳥など Various detectors have been devised in order to distinguish TGM-generated text from the human-written text. 『ストリクスヘイヴン：魔法学院』新カード情報：手札と墓地を交換するインスタントや魔技でトークンを生成する護法持ちの人間など. ジェネラルを生かすなら 「トリン」はほぼ毎自ターンにトークン生成は可能ですが トークン生成スピードが遅いため、軸にするなら「シルヴァー」になりそう。 煽り返されてちょっとメンタルダメージ受けてるやん, ウィザーブルーム黒緑5飛行で In this study, we performed fine-tuning of RoBERTa and successfully identified web pages generated by GPT-2, the largest number of parameters, with an accuracy of approximately 95%, demonstrating state-of-the-art performance. To avoid the problems that occur with unrestricted sampling, we limit the number of tokens sampled to the top k. The appropriate value of k, in this case, depends on the context, e.g., it may need to be set higher if we are predicting nouns (large vocabulary) and lower if we are predicting prepositions (small vocabulary). 投稿日： 2021年4月2日 投稿者： ゆうやん@管理人 カテゴリー：新カード情報 // 89 Comments different names of the actors in the movie reviews), Irrelevant content (e.g., words unrelated to music appear in music reviews), Contains a contradiction (A likes B but doesn't like B, etc. Here we describe the challenges of state-of-the-art detectors using the RoBERTa model. CaboChaで解析した構文木内のトークン（形態素）を出力するには、まずParser.parse()で構文木を生成します。 それからTree.size()の数だけループを回し、Tree.token()にインデックスを渡してトークンを取得します。 Based on these ideas, the classification is based on the rate of occurrence of the word, the rank of the word in the predictive distribution, and entropy. (1) Human evaluators are better at noticing inconsistencies and errors in meaning (e.g., incoherence) in automatically generated sentences. ゲームバージョン 8.2.0 . As a simple example, we use TGM to evaluate the log-likelihoods. Here we are fine-tuning a pre-trained model to perform automatic sentence detection. It can be seen that many of the tokens of high rank in the distribution appear in the auto-generated text, highlighting the difference between the two texts. A simple model using logistic regression models and tf-idf distinguishes between text in WebText and text generated by GPT-2. As a result, we found that the higher the number of parameters, the more difficult it was to detect, and that nuclear sampling was more difficult to detect than top-k sampling. ), the model can be adjusted to generate text containing specific attributes. It can be found in $p_\theta(x_t|x_1,...,x_i,... ,x_i,... ,x_{t_1})$. This article gives a comprehensive description of the text generation model and the detection of automatically generated sentences (or texts). ), The results showed that the model composition was predictable (compared to the random case) with a high probability. Automatic Detection of Machine Generated Text: A Critical Survey, Giant Language model Test Room (GLTR) tool, Differences in human and machine detector.
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