alabnii/sciclaimeval-shared-task huggingface dataset:
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\toprule \multirow{3}{*}{Target} & \multicolumn{4}{c}{GLTR} & \multicolumn{1}{c}{\multirow{3}{*}{DetectGPT}} & \multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}OpenAI-GPT2 \\ Detector\end{tabular}} & \multicolumn{2}{c}{\multirow{2}{*}{ConDA \textit{(ours)}}} \\
\cmidrule(lr){2-5} & \multirow{2}{*}{log p(x)} & \multirow{2}{*}{Rank} & \multirow{2}{*}{LogRank} & \multirow{2}{*}{Entropy} & \multicolumn{1}{c}{} & & \multicolumn{2}{c}{} \\
\cmidrule(l){8-9} & & & & & \multicolumn{1}{c}{} & & \multicolumn{1}{c}{\textit{Avg.}} & \textit{Max. \tiny (Source)} \\
\midrule C & 0.951 & 0.849 & \textbf{0.956} & 0.379 & 0.793 & 0.366 & 0.9442 & \textbf{1.00} \tiny (GM) \\
F19 & 0.558 & 0.618 & 0.546 & 0.656 & 0.5045 & 0.464 & \textbf{0.8118} & \textbf{0.966} \tiny (G2X) \\
G2X & 0.485 & 0.508 & 0.48 & 0.631 & 0.529 & 0.88 & \textbf{0.9656} & \textbf{0.998} \tiny (F19) \\
G3 & 0.362 & 0.356 & 0.341 & 0.756 & 0.5485 & 0.73 & \textbf{0.9676} & \textbf{0.982} \tiny (GM) \\
GM & 0.434 & 0.469 & 0.434 & 0.592 & 0.5415 & 0.659 & \textbf{0.9464} & \textbf{0.988} \tiny (C) \\
X & 0.473 & 0.762 & 0.442 & 0.696 & 0.7355 & 0.873 & \textbf{0.9308} & \textbf{0.999} \tiny (GM,F19) \\
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\caption{Table 4: Performance of ConDA in comparison to unsupervised baselines,
as AUROC. For ConDA, we report the average AUROC over all sources (for each target)
and also the maximum AUROC (across all sources), along with the corresponding
source in parentheses. \textbf{Bold} shows superior performance across each target.}
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