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mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.12.hook_mlp_out.json

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mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.13.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 419, "1": 247, "2": 0, "3": 29}, "net_accuracy": 0.23028495692511597}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.13.hook_resid_post.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 628, "1": 0, "2": 0, "3": 20}, "net_accuracy": 0.2147117296222664}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.16.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 253, "1": 8, "2": 0, "3": 485}, "net_accuracy": 0.24718356527501656}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.16.hook_resid_post.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 645, "1": 0, "2": 0, "3": 1}, "net_accuracy": 0.2140490390987409}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.17.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 15, "1": 286, "2": 102, "3": 381}, "net_accuracy": 0.2597746852220013}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.19.hook_resid_post.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 616, "1": 6, "2": 0, "3": 32}, "net_accuracy": 0.21669980119284293}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.2.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 414, "1": 0, "2": 0, "3": 294}, "net_accuracy": 0.2345924453280318}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.2.hook_resid_post.json

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mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.21.hook_resid_post.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 598, "1": 7, "2": 0, "3": 63}, "net_accuracy": 0.22133863485752153}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.22.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 0, "1": 2, "2": 0, "3": 870}, "net_accuracy": 0.28893306825712395}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.23.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 295, "1": 261, "2": 146, "3": 1}, "net_accuracy": 0.23293571901921803}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.24.hook_resid_post.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 359, "1": 44, "2": 0, "3": 307}, "net_accuracy": 0.23525513585155733}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.26.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 293, "1": 384, "2": 27, "3": 0}, "net_accuracy": 0.2332670642809808}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.27.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 0, "1": 320, "2": 426, "3": 0}, "net_accuracy": 0.24718356527501656}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.3.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 489, "1": 88, "2": 88, "3": 12}, "net_accuracy": 0.22432074221338635}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.33.hook_resid_post.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 21, "1": 653, "2": 46, "3": 9}, "net_accuracy": 0.2415506958250497}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.36.hook_resid_post.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 98, "1": 143, "2": 286, "3": 240}, "net_accuracy": 0.2541418157720345}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.39.hook_resid_post.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 100, "1": 209, "2": 88, "3": 406}, "net_accuracy": 0.2660702451954937}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.40.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 39, "1": 100, "2": 633, "3": 0}, "net_accuracy": 0.25579854208084823}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.40.hook_resid_post.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 137, "1": 154, "2": 214, "3": 289}, "net_accuracy": 0.26308813783962887}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.41.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 31, "1": 2, "2": 671, "3": 90}, "net_accuracy": 0.26308813783962887}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.44.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 645, "1": 0, "2": 0, "3": 0}, "net_accuracy": 0.21371769383697814}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.45.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 640, "1": 0, "2": 7, "3": 0}, "net_accuracy": 0.21438038436050363}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.45.hook_resid_post.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 417, "1": 12, "2": 248, "3": 0}, "net_accuracy": 0.22432074221338635}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.46.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 0, "1": 719, "2": 0, "3": 0}, "net_accuracy": 0.23823724320742212}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.6.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 260, "1": 0, "2": 6, "3": 488}, "net_accuracy": 0.24983432736911862}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.7.hook_mlp_out.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 441, "1": 33, "2": 6, "3": 224}, "net_accuracy": 0.2332670642809808}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.7.hook_resid_post.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 611, "1": 0, "2": 0, "3": 42}, "net_accuracy": 0.21636845593108017}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_blocks.8.hook_resid_post.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 601, "1": 0, "2": 0, "3": 52}, "net_accuracy": 0.21636845593108017}

mmlu_STEM/few_shot_5/gpt2-xl/last_token_relative_depth_100/accuracy_ln_final.hook_normalized.json

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+{"total_per_class": {"0": 645, "1": 719, "2": 783, "3": 871}, "correct_per_class": {"0": 141, "1": 377, "2": 120, "3": 59}, "net_accuracy": 0.2309476474486415}