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Thank you for sharing this sad update. It began early in June. Diego would leave the litter box with a wet back end. I'd have to try to catch him and dry him off. He was also being aggressive towards Cali so after consultation with the... Had Cali at the vet for a health check and shots. Also had the vet look at the tw...
{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nChange the tone of this text to be more relaxed and friendly.\nOutput the full new text with no extra statements or commentations.\nBegin directly with the text itself. Do not add a title, a heading, or a label naming what you have written." ], "use_multi...
Hey there! 😊 So, a little update on our furry family. Diego started having a bit of a wet back end in early June, and I’d have to gently dry him off after he left the litter box. He was also getting a bit feisty with Cali, so we took them both to the vet for a check-up and some shots. Good news – one of Cali’s lumps ...
cyankiwi/granite-4.1-8b-AWQ-INT4
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Hey there! 😊 So, a little update on our furry family. Diego started having a bit of a wet back end in early June, and I’d have to gently dry him off after he left the litter box. He was also getting a bit feisty with Cali, so we took them both to the vet for a check-up and some shots. Good news – one of Cali’s lumps o...
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CLXVI Sanjaya said, Karna, the son of Vikartana O king, resisted the mighty car-warrior Sahadeva in that battle, who advanced from desire of getting at Drona. Piercing the son of Radha with nine shafts, Sahadeva once more pierced that warrior with nine straight arrows. Karna then pierced Sahadeva in return with a hundr...
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CLXVI Sanjaya said, Karna, the son of Vikartana O king, resisted the mighty car-warrior Sahadeva in that battle, who advanced from desire of getting at Drona. Piercing the son of Radha with nine shafts, Sahadeva once more pierced that warrior with nine straight arrows. Karna then pierced Sahadeva in return with a hundr...
cyankiwi/granite-4.1-8b-AWQ-INT4
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CLXVI Sanjaya said, Karna, the son of Vikartana O king, resisted the mighty car-warrior Sahadeva in that battle, who advanced from desire of getting at Drona. Piercing the son of Radha with nine shafts, Sahadeva once more pierced that warrior with nine straight arrows. Karna then pierced Sahadeva in return with a hundr...
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This processor is one of the most famous one in guitar racks and old studios. It was introduced in 1985 and quickly became successful, it was relatively cheap, and has some very interesting effects algorithms. One of them is the famous ‘Pitch Change C’ algorithm, used by many legendary session guitarists in that era, i...
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I recently decided to dive into the world of vintage synthesizers, specifically a classic Roland Juno-6 that I stumbled upon at a garage sale. This synth, introduced in the early '80s, is renowned for its lush analog sounds and iconic filter sweeps, making it a staple in both electronic and pop music during its heyday....
cyankiwi/granite-4.1-8b-AWQ-INT4
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I recently decided to dive into the world of vintage synthesizers, specifically a classic Roland Juno-6 that I stumbled upon at a garage sale. This synth, introduced in the early '80s, is renowned for its lush analog sounds and iconic filter sweeps, making it a staple in both electronic and pop music during its heyday....
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This post explains the difference between foodservice operations that are outsourced to contract management companies and those that are 'self-operated' (self-op). You’re definitely going to want to sell to these folks. It’s not an either/or, self-op or contract-managed, situation. You’re going to want to go after both...
{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nChange the tone of this text to be more empathetic and understanding.\nOutput the full new text with no extra statements or commentations.\nBegin directly with the text itself. Do not add a title, a heading, or a label naming what you have written." ], "u...
It's truly exciting to consider the unique opportunities that both outsourced and self-operated foodservice operations present for your business. Understanding the distinctions between these two approaches can guide you in crafting a strategy that resonates deeply with each type of institution, allowing you to build me...
cyankiwi/granite-4.1-8b-AWQ-INT4
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It's truly exciting to consider the unique opportunities that both outsourced and self-operated foodservice operations present for your business. Understanding the distinctions between these two approaches can guide you in crafting a strategy that resonates deeply with each type of institution, allowing you to build me...
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Personalized baskets filled with a selection of foods, personal care products and specialty gifts, hand-crafted in Maine. Biologics, or drugs derived from living cells, often are deemed to be specialty drugs. In the Extensions tab, SDKs that apply to the project from which the Reference Manager dialog box was invoked a...
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The style of writing in this text is characterized by a highly eclectic and disjointed mixture of topics, lacking a coherent structure or thematic continuity. Qualitatively, the text exhibits: 1. **Lack of Cohesion**: The content jumps abruptly from one subject to another without logical transitions, making it difficu...
It got pretty addicting to watch the biologics market expand, especially with the new Gunsmith system that allowed for more precise modifications. Meanwhile, I was browsing through some personal care products at the local store, wondering if the latest shampoo would really make a difference in my hair's texture. As I s...
cyankiwi/granite-4.1-8b-AWQ-INT4
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It got pretty addicting to watch the biologics market expand, especially with the new Gunsmith system that allowed for more precise modifications. Meanwhile, I was browsing through some personal care products at the local store, wondering if the latest shampoo would really make a difference in my hair's texture. As I s...
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The indie comedy "Me," recently of the Marfa Film Festival, is an always amusing and frequently hilarious commentary on reality shows. The blurred lines between reality and "reality" include writer/producer/director/star Jefery Levy, who is also the creative force behind the recently reviewed sensual drama "The Key," p...
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The indie comedy "Me," recently of the Marfa Film Festival, is an always amusing and frequently hilarious commentary on reality shows. The blurred lines between reality and "reality" include writer/producer/director/star Jefery Levy, who is also the creative force behind the recently reviewed sensual drama "The Key," p...
The indie comedy "Me," recently of the Marfa Film Festival, is an always amusing and frequently hilarious commentary on reality shows. The blurred lines between reality and "reality" include writer/producer/director/star Jefery Levy, who is also the creative force behind the recently reviewed sensual drama "The Key," p...
cyankiwi/granite-4.1-8b-AWQ-INT4
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The indie comedy "Me," recently of the Marfa Film Festival, is an always amusing and frequently hilarious commentary on reality shows. The blurred lines between reality and "reality" include writer/producer/director/star Jefery Levy, who is also the creative force behind the recently reviewed sensual drama "The Key," p...
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Hello all Have only kept for maybe 9/10 games across the last couple of years, but as of next season Ill be keeping full time so am going to be after a new pair of gloves. Been using a pair of Youth Sized B3s but they feel a little too small and havent softened up at all. The problem is that whilst youth sized are a bi...
{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nEdit this text by replacing, removing or inserting twenty to thirty words, without changing anything else.\nOutput the full new text with no extra statements or commentations.\nBegin directly with the text itself. Do not add a title, a heading, or a label nam...
Hello all, I've only kept records for about 9 out of 10 games over the past couple of years, but starting next season, I'll be keeping full-time and need a new pair of gloves. I've been using Youth Sized B3 gloves, but they feel too small and haven't softened up. The issue is that while youth sizes are cramped, regula...
I've only kept records for about 9 out of 10 games over the past couple of years, but starting next season, I'll be keeping full-time and need a new pair of gloves. I've been using Youth Sized B3 gloves, but they feel too small and haven't softened up. The issue is that while youth sizes are cramped, regular men's size...
cyankiwi/granite-4.1-8b-AWQ-INT4
{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}
I've only kept records for about 9 out of 10 games over the past couple of years, but starting next season, I'll be keeping full-time and need a new pair of gloves. I've been using Youth Sized B3 gloves, but they feel too small and haven't softened up. The issue is that while youth sizes are cramped, regular men's size...
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'Stars of Science' regularly showcases inventions across multiple disciplines and sectors that help communities in the Arab world. Multiple contestants throughout the history of the show have developed inventions that focus on preserving their communities’ identity and culture. Two contestants on the current season 13 ...
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'Stars of Science' regularly showcases inventions across multiple disciplines and sectors that help communities in the Arab world. Multiple contestants throughout the history of the show have developed inventions that focus on preserving their communities’ identity and culture. Two contestants on the current season 13...
cyankiwi/granite-4.1-8b-AWQ-INT4
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'Stars of Science' regularly showcases inventions across multiple disciplines and sectors that help communities in the Arab world. Multiple contestants throughout the history of the show have developed inventions that focus on preserving their communities’ identity and culture. Two contestants on the current season 13 ...
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I didn t want to those services, but three jobs statewide. Book offer great places to sit at 0.6 miles 15 years, mandarin. Regulators informally vetoed a planned expansion, announced at a bath seneca model! Additional fees are working closely reviewing cases of this year on the first day, there s website. Still take ad...
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Mgp will remain. Typically, if you can result. Have disease control board. Mail-In-Only voting ended march 16. Shares surged to mgm national harbor resort spa salon. Ricky martin: the city casino resort and human services. Mlb and washington and four shows and the novel coronavirus. Sometimes it will be pushed some cer...
cyankiwi/granite-4.1-8b-AWQ-INT4
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Mgp will remain. Typically, if you can result. Have disease control board. Mail-In-Only voting ended march 16. Shares surged to mgm national harbor resort spa salon. Ricky martin: the city casino resort and human services. Mlb and washington and four shows and the novel coronavirus. Sometimes it will be pushed some cer...
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As a follow up from last week, Monday evening the French President Macron as expected extended our lockdown period to May 11, 2020. However, with this came discussion about how France would slowly start to reopen on that date. A phased in approach starting with getting kids back to school and other industries starting ...
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上周的后续行动中，周一晚上，法国总统马克龙如预期般将封锁期延长至2020年5月11日。然而，随之而来的是关于法国何时逐步恢复运作的讨论。 采取分阶段方法，首先让孩子们回到学校，并让其他行业重新开放，引发了乐观情绪。从旅游/旅游角度来看，马克龙尚未解除餐馆的限制，非欧洲人（不包括英国）进入法国进行非必要旅行的国际边境仍然关闭。 这意味着什么？ 目前还没有人能确定。我们知道两家英国航空公司已经建议，非必要旅行的航班将于6月中旬重新启动。欧盟委员会建议在欧洲内部恢复旅行，这对于仍希望今年与我们旅行的英国和欧洲客户来说是好消息。更令人振奋的消息，对于非欧洲客户在秋季有一丝希望——即《困难之路》（Tour de France）将于2020...
On Monday night, French President Macron as expected extended the lockdown until May 11, 2020. However, this has sparked discussions about when France will gradually resume operations. Taking a phased approach, first allowing children to return to school and then reopening other sectors has generated optimistic sentim...
cyankiwi/granite-4.1-8b-AWQ-INT4
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On Monday night, French President Macron as expected extended the lockdown until May 11, 2020. However, this has sparked discussions about when France will gradually resume operations. Taking a phased approach, first allowing children to return to school and then reopening other sectors has generated optimistic sentime...
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When I committed to NaBloPoMo, I’m not sure that I realized the commitment it would entail. Well, sure, I knew that it meant that I would post daily. It meant that I would be able to refresh my blog with a multitude of recipes that I’ve been dying to share, but for one reason or another, hadn’t blogged yet. The main re...
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The author employs a warm, inviting, and descriptive language style, characterized by the following elements: 1. **Personal Tone**: The author uses a first-person narrative, sharing personal reflections and motivations behind committing to NaBloPoMo, which creates an intimate connection with the reader. 2. **Reflecti...
I’m not sure that I realized the commitment it would entail when I decided to take on NaBloPoMo, but here I am, ready to dive in. Life is busy, and sometimes it’s more convenient to write on a Sunday rather than during the hectic weekdays. Today, I want to share something personal—my journey with blogging and a little ...
cyankiwi/granite-4.1-8b-AWQ-INT4
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I’m not sure that I realized the commitment it would entail when I decided to take on NaBloPoMo, but here I am, ready to dive in. Life is busy, and sometimes it’s more convenient to write on a Sunday rather than during the hectic weekdays. Today, I want to share something personal—my journey with blogging and a little ...
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0.925935
4.510257
1.392892
0.045741
0.029968
-1.884659
4.22261
1.600844
0.025237
0.026814
2.485772
0.718811
0.807867
0
0.040475
3
0.733539
- Bright Star reviews, ratings etc. *SPOILERS* by Saturn » Sun May 17, 2009 2:56 pm - 312 Replies - 1189380 Views - Last post by Pjerrot Sun Jun 06, 2010 11:38 pm - - Bright Star the movie website! by Saturn » Thu Jun 12, 2008 9:11 pm - 47 Replies - 185336 Views - Last post by Raphael Thu Nov 26, 2009 3:27 pm - - Discu...
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Bright Star reviews, ratings etc. *SPOILERS* by Saturn » Sun May 17, 2009 2:56 pm - 312 Replies - 1189380 Views - Last post by Pjerrot Sun Jun 06, 2010 11:38 pm Bright Star the movie website! by Saturn » Thu Jun 12, 2008 9:11 pm - 47 Replies - 185336 Views - Last post by Raphael Thu Nov 26, 2009 ...
cyankiwi/granite-4.1-8b-AWQ-INT4
{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}
Bright Star reviews, ratings etc. *SPOILERS* by Saturn » Sun May 17, 2009 2:56 pm - 312 Replies - 1189380 Views - Last post by Pjerrot Sun Jun 06, 2010 11:38 pm Bright Star the movie website! by Saturn » Thu Jun 12, 2008 9:11 pm - 47 Replies - 185336 Views - Last post by Raphael Thu Nov 26, 2009 3:27 pm Discussion abou...
0.022857
0.093278
162
0.000119
0.006475
0.020975
0.018915
0.019946
0.22074
-7.8125
4.211267
1.436177
0.023256
0.06538
-0.057009
3.770522
1.358182
0.014919
0.057043
1.15384
4.372434
1.463621
0.025397
0.066667
-0.420288
3.916807
1.396796
0.014512
0.057596
1.143624
0.84123
0.849453
0
0.037114
0
0.03237
CAPSULE: Kaiju Noir. With the Japanese franchise on the King of the Monsters in hiatus, the Godzilla character is being loaned to Warner Brothers so that Gareth Edwards can make an American Godzilla film. This is a script whose drama is better than Toho's usual fare, but audiences may find the new film is dark and drab...
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The new American Godzilla film, directed by Gareth Edwards and starring Bryan Cranston, marks a significant departure from Toho's traditional kaiju fare, offering a darker, more somber narrative that revisits the original film's themes of tragedy and nuclear consequence. In this version, the opening scene shifts focus ...
In this new American Godzilla film, directed by Gareth Edwards and starring Bryan Cranston, a significant departure from Toho's traditional kaiju fare is evident, offering a darker, more somber narrative that revisits the original film's themes of tragedy and nuclear consequence, as the opening scene shifts focus to a ...
cyankiwi/granite-4.1-8b-AWQ-INT4
{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}
In this new American Godzilla film, directed by Gareth Edwards and starring Bryan Cranston, a significant departure from Toho's traditional kaiju fare is evident, offering a darker, more somber narrative that revisits the original film's themes of tragedy and nuclear consequence, as the opening scene shifts focus to a ...
0.872564
0.97343
4,541
0.895899
0.283138
0.161746
0.187128
0.174632
0.651956
-6.0625
20.046873
3.04344
0.05098
0.131765
0.670267
15.815705
2.680367
0.058824
0.111373
-1.282028
15.370043
2.726381
0.044496
0.11007
-0.056481
14.011865
2.664633
0.030445
0.100703
0.2361
0.803702
0.848266
0
0.023796
3
0.988863
Bol Entertainment is an upcoming Pakistani channel that is a subsidiary project of Axact organization, which is the world’s fastest leading Karachi-based IT company. Pakistan Electronic Media Regulatory Authority (PEMRA) has approved radio and satellite television licenses to the BOL Network. The founders of the channe...
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Hey there! So, you might have heard about Bol Entertainment, the super cool new Pakistani channel that's part of the awesome Axact organization. Axact is this super-fast IT company based in Karachi, and they're really shaking things up. The Pakistan Electronic Media Regulatory Authority (PEMRA) just gave the thumbs-up ...
BOL Entertainment, the innovative new Pakistani channel under the umbrella of the dynamic Axact organization, is indeed generating a lot of excitement. Axact, a rapidly growing IT company headquartered in Karachi, is at the forefront of transforming digital landscapes. With the recent approval from the Pakistan Electro...
cyankiwi/granite-4.1-8b-AWQ-INT4
{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}
BOL Entertainment, the innovative new Pakistani channel under the umbrella of the dynamic Axact organization, is indeed generating a lot of excitement. Axact, a rapidly growing IT company headquartered in Karachi, is at the forefront of transforming digital landscapes. With the recent approval from the Pakistan Electro...
0.848889
0.946328
1,656
0.869361
0.191256
0.240841
0.125479
0.187233
0.719341
2.375
28.105714
2.704589
0.063158
0.168421
-2.611264
22.5573
2.542473
0.115789
0.136842
-2.523425
7.200448
1.964177
0.032698
0.046322
-0.103899
6.38383
2.012511
0.021798
0.049046
1.661662
1.019556
0.807326
1
0.25899
3
0.999501
SAT Practice: Essay - Amazon Web Services. Scores for the samples provided below were assigned on a 1-4 scale according to the redesigned SAT Essay Scoring Rubric. It is important to note that although these are representative samples of student ability at each score point, the set itself does not exhaustively illustra...
{ "chat_turns": [ "<document>\n{{DOC}}\n</document>\n\nChange the tone of this text to be more suited for a business audience.\nOutput the full new text with no extra statements or commentations.\nBegin directly with the text itself. Do not add a title, a heading, or a label naming what you have written." ], ...
SAT Practice: Essay - Amazon Web Services. Scores for the samples provided below were assigned on a 1-4 scale according to the redesigned SAT Essay Scoring Rubric. These samples represent student ability at each score point but do not exhaustively cover the range of skills in Reading, Analysis, and Writing associated ...
cyankiwi/granite-4.1-8b-AWQ-INT4
{"temperature": 0.6, "top_p": 0.9, "presence_penalty": 1.5, "extra_body": {"top_k": 40}}
SAT Practice: Essay - Amazon Web Services. Scores for the samples provided below were assigned on a 1-4 scale according to the redesigned SAT Essay Scoring Rubric. These samples represent student ability at each score point but do not exhaustively cover the range of skills in Reading, Analysis, and Writing associated w...
0.717557
0.864211
2,662
0.312343
0.042347
0.098328
0.139089
0.11918
0.528709
-7.4375
15.777492
2.535348
0.065817
0.096603
-3.07108
8.389224
2.110075
0.044586
0.069002
-0.254875
19.837715
2.816855
0.05
0.114
-1.663618
19.182255
2.544328
0.068
0.12
-4.191767
0.608575
0.904538
0
0.008327
1
0.283292
End of preview. Expand in Data Studio
Auto-generated Fastdetector dataset

CC-2021 Stat

Analysis of G-reen/cc-2021-stat

Best detector
EditLens Roberta-Large Score
0.3588TPR
Hardest prompt subset
rewrite
0.0536mean TPR · 13 detectors
Hardest generator config
gemma-4-E4B-it (Temp: 0.7)
0.0693mean TPR · 13 detectors
Human · originalAI · final_response
23,214
rows
7
generator configs
4
prompt subsets
13
detectors
20,892 / 2,322
eval / validation
01

Detector leaderboard

All detectors, ranked by overall AUROC. The bar starts at the chance line (0.5): it runs right in teal when a detector separates the classes and left in clay when its scores point the wrong way. TPR, FPR, accuracy and F1 are measured at the listed threshold. Click any row to open that detector's full report.

#
Detector
AUROC
TPR
FPR
Acc.
F1
Threshold
01
EditLens Roberta-Large Score ▲ best
Trained detector · RoBERTa-Large
0.8775
0.3588
0.0046
0.6771
0.5263
0.5961
FPR ≤ 0.5%
⌄
SubsetNAUROCTPRFPRAcc.F1
Overall41,784
0.8775
0.35880.00460.67710.5263
promptdirect_reference10,356
0.8405
0.41810.00410.70700.5880
promptindirect_reference10,182
0.9333
0.43310.00550.71380.6021
promptrevise10,666
0.9662
0.45680.00390.72640.6254
promptrewrite ▼ worst10,580
0.7711
0.13040.00490.56280.2298
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6)6,064
0.8177
0.26420.00400.63010.4166
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25)6,004
0.8537
0.16660.00700.57980.2838
genMinistral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6) ▲ best5,860
0.9907
0.64510.00340.82080.7826
genQwen3-8B-AWQ (Temp: 0.7)6,118
0.8077
0.29580.00720.64430.4541
gengemma-4-E4B-it (Temp: 0.7)5,972
0.8523
0.25150.00270.62440.4011
gengranite-4.1-8b-AWQ-INT4 (Temp: 0.6)5,876
0.9029
0.43640.00240.71700.6066
gengranite-4.1-8b-AWQ-INT4 (Temp: 1.25)5,890
0.9249
0.46420.00540.72940.6317
direction: higher_is_aiswept for: fpr_0_5pctthreshold: 0.5961
EditLens Roberta-Large Score: score histograms, overall and per subset

Human (blue) and AI (orange) score distributions on the evaluation split. The dashed line is the decision threshold.Open full size ↗

02
EditLens Roberta-Large Bucket
Trained detector · RoBERTa-Large
0.8145
0.6656
0.0513
0.8072
0.7754
0.0000
best F1
⌄
SubsetNAUROCTPRFPRAcc.F1
Overall41,784
0.8145
0.66560.05130.80720.7754
promptdirect_reference10,356
0.7897
0.61240.05080.78080.7364
promptindirect_reference10,182
0.8516
0.73620.05170.84230.8236
promptrevise10,666
0.9239
0.87850.04910.91470.9115
promptrewrite ▼ worst10,580
0.6928
0.43520.05370.69070.5846
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6)6,064
0.7088
0.45710.05080.70320.6063
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25)6,004
0.7709
0.59360.05660.76850.7194
genMinistral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6) ▲ best5,860
0.9743
0.97030.04950.96040.9608
genQwen3-8B-AWQ (Temp: 0.7)6,118
0.7343
0.50930.05170.72880.6526
gengemma-4-E4B-it (Temp: 0.7)5,972
0.7853
0.61020.04920.78050.7354
gengranite-4.1-8b-AWQ-INT4 (Temp: 0.6)5,876
0.8527
0.73720.05240.84240.8239
gengranite-4.1-8b-AWQ-INT4 (Temp: 1.25)5,890
0.8838
0.79760.04890.87440.8639
direction: higher_is_aiswept for: f1threshold: 0.0000
EditLens Roberta-Large Bucket: score histograms, overall and per subset

Human (blue) and AI (orange) score distributions on the evaluation split. The dashed line is the decision threshold.Open full size ↗

03
Binoculars
Zero-shot
0.6302
0.0121
0.0031
0.5045
0.0239
1.0472
FPR ≤ 0.5%
⌄
SubsetNAUROCTPRFPRAcc.F1
Overall41,784
0.6302
0.01210.00310.50450.0239
promptdirect_reference10,356
0.6006
0.01140.00270.50430.0225
promptindirect_reference10,182
0.6500
0.01920.00370.50780.0376
promptrevise10,666
0.6654
0.00750.00340.50210.0148
promptrewrite10,580
0.6046
0.01060.00260.50400.0209
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6)6,064
0.5311
0.00130.00360.49880.0026
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25) ▲ best6,004
0.8816
0.04030.00330.51850.0772
genMinistral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6)5,860
0.5429
0.00920.00380.50270.0182
genQwen3-8B-AWQ (Temp: 0.7) ▼ worst6,118
0.4838
0.00130.00360.49890.0026
gengemma-4-E4B-it (Temp: 0.7)5,972
0.6467
0.00740.00330.50200.0146
gengranite-4.1-8b-AWQ-INT4 (Temp: 0.6)5,876
0.6074
0.00650.00170.50240.0128
gengranite-4.1-8b-AWQ-INT4 (Temp: 1.25)5,890
0.7140
0.01900.00240.50830.0372
direction: higher_is_aiswept for: fpr_0_5pctthreshold: 1.0472
Binoculars: score histograms, overall and per subset

Human (blue) and AI (orange) score distributions on the evaluation split. The dashed line is the decision threshold.Open full size ↗

04
FastDetectGPT (Llama-3.2-3B-Instruct)
Zero-shot · Llama-3.2-3B-Instruct
0.5725
0.0027
0.0041
0.4993
0.0054
6.7770
FPR ≤ 0.5%
⌄
SubsetNAUROCTPRFPRAcc.F1
Overall41,784
0.5725
0.00270.00410.49930.0054
promptdirect_reference10,356
0.5900
0.00230.00480.49870.0046
promptindirect_reference10,182
0.6299
0.00200.00450.49870.0039
promptrevise10,666
0.5402
0.00340.00470.49930.0067
promptrewrite10,580
0.5334
0.00320.00250.50040.0064
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6) ▲ best6,064
0.6945
0.00130.00530.49800.0026
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25)6,004
0.5419
0.00800.00670.50070.0158
genMinistral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6)5,860
0.5923
0.00070.00270.49900.0014
genQwen3-8B-AWQ (Temp: 0.7)6,118
0.5891
0.00100.00160.49970.0020
gengemma-4-E4B-it (Temp: 0.7)5,972
0.5690
0.00440.00570.49930.0086
gengranite-4.1-8b-AWQ-INT4 (Temp: 0.6)5,876
0.5443
0.00170.00440.49860.0034
gengranite-4.1-8b-AWQ-INT4 (Temp: 1.25) ▼ worst5,890
0.4715
0.00200.00240.49980.0041
direction: higher_is_aiswept for: fpr_0_5pctthreshold: 6.7770
FastDetectGPT (Llama-3.2-3B-Instruct): score histograms, overall and per subset

Human (blue) and AI (orange) score distributions on the evaluation split. The dashed line is the decision threshold.Open full size ↗

05
Top-k Outliers (Llama-3.2-3B-Instruct)
Zero-shot · Llama-3.2-3B-Instruct
0.5637
0.0191
0.0033
0.5079
0.0373
0.0109
FPR ≤ 0.5%
⌄
SubsetNAUROCTPRFPRAcc.F1
Overall41,784
0.5637
0.01910.00330.50790.0373
promptdirect_reference10,356
0.6161
0.03730.00410.51660.0716
promptindirect_reference10,182
0.5662
0.03460.00180.51640.0667
promptrevise10,666
0.5998
0.00380.00340.50020.0074
promptrewrite10,580
0.4725
0.00170.00380.49900.0034
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6) ▲ best6,064
0.7196
0.06270.00200.53030.1177
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25) ▼ worst6,004
0.2595
0.00130.00530.49800.0026
genMinistral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6)5,860
0.6421
0.00100.00480.49810.0020
genQwen3-8B-AWQ (Temp: 0.7)6,118
0.6911
0.04870.00230.52320.0927
gengemma-4-E4B-it (Temp: 0.7)5,972
0.5050
0.00130.00130.50000.0027
gengranite-4.1-8b-AWQ-INT4 (Temp: 0.6)5,876
0.6051
0.01230.00440.50390.0241
gengranite-4.1-8b-AWQ-INT4 (Temp: 1.25)5,890
0.5226
0.00410.00270.50070.0081
direction: lower_is_aiswept for: fpr_0_5pctthreshold: 0.0109
Top-k Outliers (Llama-3.2-3B-Instruct): score histograms, overall and per subset

Human (blue) and AI (orange) score distributions on the evaluation split. The dashed line is the decision threshold.Open full size ↗

06
Top-p Outliers (Llama-3.2-3B-Instruct)
Zero-shot · Llama-3.2-3B-Instruct
0.5598
0.0108
0.0030
0.5039
0.0213
0.0158
FPR ≤ 0.5%
⌄
SubsetNAUROCTPRFPRAcc.F1
Overall41,784
0.5598
0.01080.00300.50390.0213
promptdirect_reference10,356
0.5837
0.01600.00290.50660.0315
promptindirect_reference10,182
0.5977
0.02460.00290.51080.0478
promptrevise10,666
0.5320
0.00170.00360.49910.0034
promptrewrite10,580
0.5280
0.00170.00260.49950.0034
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6) ▲ best6,064
0.6963
0.05010.00300.52360.0952
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25)6,004
0.5704
0.00430.00400.50020.0086
genMinistral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6)5,860
0.5563
0.00100.00270.49910.0020
genQwen3-8B-AWQ (Temp: 0.7)6,118
0.6147
0.01110.00260.50420.0219
gengemma-4-E4B-it (Temp: 0.7)5,972
0.5118
0.00440.00200.50120.0087
gengranite-4.1-8b-AWQ-INT4 (Temp: 0.6)5,876
0.5298
0.00270.00240.50020.0054
gengranite-4.1-8b-AWQ-INT4 (Temp: 1.25) ▼ worst5,890
0.4325
0.00100.00440.49830.0020
direction: lower_is_aiswept for: fpr_0_5pctthreshold: 0.0158
Top-p Outliers (Llama-3.2-3B-Instruct): score histograms, overall and per subset

Human (blue) and AI (orange) score distributions on the evaluation split. The dashed line is the decision threshold.Open full size ↗

07
Perplexity (Llama-3.2-3B-Instruct)
Zero-shot · Llama-3.2-3B-Instruct
0.5478
0.0055
0.0045
0.5005
0.0108
2.2698
FPR ≤ 0.5%
⌄
SubsetNAUROCTPRFPRAcc.F1
Overall41,784
0.5478
0.00550.00450.50050.0108
promptdirect_reference10,356
0.5778
0.00620.00520.50050.0122
promptindirect_reference10,182
0.5590
0.01320.00270.50520.0259
promptrevise10,666
0.5867
0.00130.00510.49810.0026
promptrewrite10,580
0.4672
0.00150.00470.49840.0030
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6) ▲ best6,064
0.7393
0.02540.00300.51120.0494
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25) ▼ worst6,004
0.2114
0.00070.00570.49750.0013
genMinistral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6)5,860
0.6083
0.00070.00650.49710.0014
genQwen3-8B-AWQ (Temp: 0.7)6,118
0.6866
0.00460.00330.50070.0091
gengemma-4-E4B-it (Temp: 0.7)5,972
0.4750
0.00200.00330.49930.0040
gengranite-4.1-8b-AWQ-INT4 (Temp: 0.6)5,876
0.6144
0.00370.00510.49930.0074
gengranite-4.1-8b-AWQ-INT4 (Temp: 1.25)5,890
0.4990
0.00070.00440.49810.0014
direction: lower_is_aiswept for: fpr_0_5pctthreshold: 2.2698
Perplexity (Llama-3.2-3B-Instruct): score histograms, overall and per subset

Human (blue) and AI (orange) score distributions on the evaluation split. The dashed line is the decision threshold.Open full size ↗

08
Entropy (Llama-3.2-3B-Instruct)
Zero-shot · Llama-3.2-3B-Instruct
0.5391
0.0032
0.0044
0.4994
0.0063
0.7575
FPR ≤ 0.5%
⌄
SubsetNAUROCTPRFPRAcc.F1
Overall41,784
0.5391
0.00320.00440.49940.0063
promptdirect_reference10,356
0.5668
0.00290.00500.49890.0057
promptindirect_reference10,182
0.5409
0.00770.00260.50260.0152
promptrevise10,666
0.5799
0.00090.00470.49810.0019
promptrewrite10,580
0.4678
0.00130.00510.49810.0026
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6) ▲ best6,064
0.7033
0.01190.00260.50460.0234
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25) ▼ worst6,004
0.2467
0.00030.00530.49750.0007
genMinistral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6)5,860
0.5743
0.00070.00580.49740.0014
genQwen3-8B-AWQ (Temp: 0.7)6,118
0.6652
0.00390.00330.50030.0078
gengemma-4-E4B-it (Temp: 0.7)5,972
0.4608
0.00170.00330.49920.0033
gengranite-4.1-8b-AWQ-INT4 (Temp: 0.6)5,876
0.6065
0.00240.00510.49860.0047
gengranite-4.1-8b-AWQ-INT4 (Temp: 1.25)5,890
0.5167
0.00100.00510.49800.0020
direction: lower_is_aiswept for: fpr_0_5pctthreshold: 0.7575
Entropy (Llama-3.2-3B-Instruct): score histograms, overall and per subset

Human (blue) and AI (orange) score distributions on the evaluation split. The dashed line is the decision threshold.Open full size ↗

09
Top-p Outliers (Llama-3.2-3B)
Zero-shot · Llama-3.2-3B
0.5168
0.0062
0.0071
0.4995
0.0122
0.0054
FPR ≤ 0.5%
⌄
SubsetNAUROCTPRFPRAcc.F1
Overall41,784
0.5168
0.00620.00710.49950.0122
promptdirect_reference10,356
0.6107
0.01000.00770.50120.0197
promptindirect_reference10,182
0.5249
0.01280.00630.50320.0251
promptrevise10,666
0.4953
0.00090.00750.49670.0019
promptrewrite10,580
0.4396
0.00130.00680.49730.0026
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6) ▲ best6,064
0.6752
0.01680.00530.50580.0329
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25) ▼ worst6,004
0.2196
0.00100.00830.49630.0020
genMinistral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6)5,860
0.6009
0.00030.00750.49640.0007
genQwen3-8B-AWQ (Temp: 0.7)6,118
0.6662
0.01440.00720.50360.0282
gengemma-4-E4B-it (Temp: 0.7)5,972
0.4851
0.00400.00400.50000.0080
gengranite-4.1-8b-AWQ-INT4 (Temp: 0.6)5,876
0.5588
0.00340.00850.49740.0067
gengranite-4.1-8b-AWQ-INT4 (Temp: 1.25)5,890
0.4100
0.00270.00880.49690.0054
direction: lower_is_aiswept for: fpr_0_5pctthreshold: 0.0054
Top-p Outliers (Llama-3.2-3B): score histograms, overall and per subset

Human (blue) and AI (orange) score distributions on the evaluation split. The dashed line is the decision threshold.Open full size ↗

10
Top-k Outliers (Llama-3.2-3B)
Zero-shot · Llama-3.2-3B
0.5168
0.0053
0.0048
0.5002
0.0104
0.0056
FPR ≤ 0.5%
⌄
SubsetNAUROCTPRFPRAcc.F1
Overall41,784
0.5168
0.00530.00480.50020.0104
promptdirect_reference10,356
0.5622
0.00850.00540.50150.0168
promptindirect_reference10,182
0.5113
0.01040.00390.50320.0205
promptrevise10,666
0.5465
0.00110.00540.49780.0022
promptrewrite10,580
0.4465
0.00130.00430.49850.0026
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6) ▲ best6,064
0.6778
0.01550.00330.50610.0304
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25) ▼ worst6,004
0.2099
0.00070.00600.49730.0013
genMinistral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6)5,860
0.5863
0.00000.00550.49730.0000
genQwen3-8B-AWQ (Temp: 0.7)6,118
0.6659
0.01470.00390.50540.0289
gengemma-4-E4B-it (Temp: 0.7)5,972
0.4514
0.00070.00270.49900.0013
gengranite-4.1-8b-AWQ-INT4 (Temp: 0.6)5,876
0.5570
0.00410.00710.49850.0081
gengranite-4.1-8b-AWQ-INT4 (Temp: 1.25)5,890
0.4683
0.00070.00510.49780.0014
direction: lower_is_aiswept for: fpr_0_5pctthreshold: 0.0056
Top-k Outliers (Llama-3.2-3B): score histograms, overall and per subset

Human (blue) and AI (orange) score distributions on the evaluation split. The dashed line is the decision threshold.Open full size ↗

11
FastDetectGPT (Llama-3.2-3B)
Zero-shot · Llama-3.2-3B
0.5159
0.0986
0.0066
0.5460
0.1784
-3.2326
FPR ≤ 0.5%
⌄
SubsetNAUROCTPRFPRAcc.F1
Overall41,784
0.5159
0.09860.00660.54600.1784
promptdirect_reference10,356
0.4288
0.07090.00700.53200.1315
promptindirect_reference10,182
0.4831
0.11590.00750.55420.2063
promptrevise10,666
0.5541
0.10010.00560.54730.1811
promptrewrite10,580
0.5944
0.10760.00620.55070.1931
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6) ▼ worst6,064
0.2939
0.00300.00820.49740.0059
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25) ▲ best6,004
0.8992
0.56730.00570.78080.7213
genMinistral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6)5,860
0.3979
0.01430.00750.50340.0281
genQwen3-8B-AWQ (Temp: 0.7)6,118
0.3076
0.00820.00620.50100.0161
gengemma-4-E4B-it (Temp: 0.7)5,972
0.5519
0.01310.00640.50330.0256
gengranite-4.1-8b-AWQ-INT4 (Temp: 0.6)5,876
0.4675
0.00990.00540.50220.0194
gengranite-4.1-8b-AWQ-INT4 (Temp: 1.25)5,890
0.6924
0.07230.00650.53290.1341
direction: lower_is_aiswept for: fpr_0_5pctthreshold: -3.2326
FastDetectGPT (Llama-3.2-3B): score histograms, overall and per subset

Human (blue) and AI (orange) score distributions on the evaluation split. The dashed line is the decision threshold.Open full size ↗

12
Entropy (Llama-3.2-3B)
Zero-shot · Llama-3.2-3B
0.4869
0.0005
0.0049
0.4978
0.0010
0.2888
FPR ≤ 0.5%
⌄
SubsetNAUROCTPRFPRAcc.F1
Overall41,784
0.4869
0.00050.00490.49780.0010
promptdirect_reference10,356
0.4755
0.00040.00620.49710.0008
promptindirect_reference10,182
0.4783
0.00100.00370.49860.0020
promptrevise10,666
0.5343
0.00000.00540.49730.0000
promptrewrite10,580
0.4567
0.00080.00420.49830.0015
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6) ▲ best6,064
0.6379
0.00030.00300.49870.0007
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25) ▼ worst6,004
0.2151
0.00030.00570.49730.0007
genMinistral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6)5,860
0.4974
0.00000.00610.49690.0000
genQwen3-8B-AWQ (Temp: 0.7)6,118
0.6106
0.00130.00420.49850.0026
gengemma-4-E4B-it (Temp: 0.7)5,972
0.4135
0.00030.00270.49880.0007
gengranite-4.1-8b-AWQ-INT4 (Temp: 0.6)5,876
0.5525
0.00100.00680.49710.0020
gengranite-4.1-8b-AWQ-INT4 (Temp: 1.25)5,890
0.4814
0.00030.00580.49730.0007
direction: lower_is_aiswept for: fpr_0_5pctthreshold: 0.2888
Entropy (Llama-3.2-3B): score histograms, overall and per subset

Human (blue) and AI (orange) score distributions on the evaluation split. The dashed line is the decision threshold.Open full size ↗

13
Perplexity (Llama-3.2-3B) ▼ worst
Zero-shot · Llama-3.2-3B
0.4854
0.0005
0.0044
0.4980
0.0010
1.2916
FPR ≤ 0.5%
⌄
SubsetNAUROCTPRFPRAcc.F1
Overall41,784
0.4854
0.00050.00440.49800.0010
promptdirect_reference10,356
0.5068
0.00040.00580.49730.0008
promptindirect_reference10,182
0.4852
0.00100.00260.49920.0020
promptrevise10,666
0.5149
0.00000.00490.49760.0000
promptrewrite10,580
0.4338
0.00060.00430.49810.0011
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 0.6) ▲ best6,064
0.6774
0.00070.00230.49920.0013
genMeta-Llama-3.1-8B-Instruct-AWQ-INT4 (Temp: 1.25) ▼ worst6,004
0.1564
0.00030.00630.49700.0007
genMinistral-3-8B-Instruct-2512-AWQ-4bit (Temp: 0.6)5,860
0.5326
0.00000.00550.49730.0000
genQwen3-8B-AWQ (Temp: 0.7)6,118
0.6481
0.00070.00290.49890.0013
gengemma-4-E4B-it (Temp: 0.7)5,972
0.4061
0.00070.00230.49920.0013
gengranite-4.1-8b-AWQ-INT4 (Temp: 0.6)5,876
0.5512
0.00070.00540.49760.0014
gengranite-4.1-8b-AWQ-INT4 (Temp: 1.25)5,890
0.4266
0.00030.00610.49710.0007
direction: lower_is_aiswept for: fpr_0_5pctthreshold: 1.2916
Perplexity (Llama-3.2-3B): score histograms, overall and per subset

Human (blue) and AI (orange) score distributions on the evaluation split. The dashed line is the decision threshold.Open full size ↗

▲ / ▼ mark the best and worst AUROC. Click a row for its per-subset performance, thresholding details, score histograms and threshold sweep. Thresholds are swept on the validation split for FPR ≤ 0.5%, except EditLens Bucket, which uses the best-F1 threshold. Zero-shot detectors use Llama-3.2-3B or Llama-3.2-3B-Instruct as the scoring model.

02

Robustness across subsets

AUROC for every detector on every prompt subset and generator config. Teal cells separate human from AI text; clay cells fall below chance, meaning the detector's scores are inverted on that slice. The bottom row is the subset's AUROC averaged over all detectors.

Prompt subsetGenerator config · temperature
DetectorOverall
direct
reference
indirect
reference
revise
rewrite
Llama 3.1 8B
T 0.6
Llama 3.1 8B
T 1.25
Ministral 3 8B
T 0.6
Qwen3 8B
T 0.7
Gemma 4 E4B
T 0.7
Granite 4.1 8B
T 0.6
Granite 4.1 8B
T 1.25
EditLens Roberta-Large Score0.880.840.930.970.770.820.850.990.810.850.900.92
EditLens Roberta-Large Bucket0.810.790.850.920.690.710.770.970.730.790.850.88
Binoculars0.630.600.650.670.600.530.880.540.480.650.610.71
FastDetectGPT (Llama-3.2-3B-Instruct)0.570.590.630.540.530.690.540.590.590.570.540.47
Top-k Outliers (Llama-3.2-3B-Instruct)0.560.620.570.600.470.720.260.640.690.510.610.52
Top-p Outliers (Llama-3.2-3B-Instruct)0.560.580.600.530.530.700.570.560.610.510.530.43
Perplexity (Llama-3.2-3B-Instruct)0.550.580.560.590.470.740.210.610.690.470.610.50
Entropy (Llama-3.2-3B-Instruct)0.540.570.540.580.470.700.250.570.670.460.610.52
Top-p Outliers (Llama-3.2-3B)0.520.610.520.500.440.680.220.600.670.490.560.41
Top-k Outliers (Llama-3.2-3B)0.520.560.510.550.450.680.210.590.670.450.560.47
FastDetectGPT (Llama-3.2-3B)0.520.430.480.550.590.290.900.400.310.550.470.69
Entropy (Llama-3.2-3B)0.490.480.480.530.460.640.220.500.610.410.550.48
Perplexity (Llama-3.2-3B)0.490.510.490.510.430.680.160.530.650.410.550.43
Mean of 13 detectors–0.600.600.620.530.660.460.620.630.550.610.57
AUROC
0 inverted · 0.5 chance · 1 perfectHover a cell for the 4-decimal value. The Overall column is outlined.
03

How far do the rewrites move?

Distributions of two distance measures between each original and its generated counterpart, overall and within each subset. The other eight measures are in the appendix.

04

Appendix

Reference material: how the dataset was built, its schema and loading code, univariate statistics, correlations and every distance measure. Each detector's full report opens from its row in the leaderboard.

How the dataset was built7 generator configs · 4 prompt families · evaluation protocol⌄

Every row pairs a human-written text (original) with text a generator model produced from it (final_response). Each generator config is stored in its own dataset config, and each row was produced by one of four families of prompts.

Generator configs

ConfigGenerator modelTempSamplingRows
shard_0cyankiwi/granite-4.1-8b-AWQ-INT40.6top_p 0.9 · top_k 40 · presence_penalty 1.53,275
shard_1hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT40.6top_p 0.9 · top_k 40 · presence_penalty 1.53,327
shard_2Qwen/Qwen3-8B-AWQ0.7top_p 0.8 · top_k 20 · presence_penalty 1.5 · thinking off3,391
shard_3google/gemma-4-E4B-it0.7top_p 0.8 · top_k -1 · presence_penalty 1.5 · thinking off3,323
shard_4cyankiwi/Ministral-3-8B-Instruct-2512-AWQ-4bit0.6top_p 0.9 · top_k 40 · presence_penalty 1.53,258
shard_5hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT41.25top_p 1.0 · top_k -1 · presence_penalty 1.5 · top_a 0.1 · nsigma 1.53,325
shard_6cyankiwi/granite-4.1-8b-AWQ-INT41.25top_p 1.0 · top_k -1 · presence_penalty 1.5 · top_a 0.1 · nsigma 1.53,315
Total23,214

Prompt subsets

Every prompt wraps the human text in <document> tags. Two-turn prompts feed the first response back into a second instruction, and final_response comes from the last turn. Counts cover all 23,214 rows.

direct_reference5,735 rows
single-turn · 3 instructions

Write a new text with the original as a reference: a similar theme, a phone-typed rough draft inspired by it, or a different topic that reuses its phrases verbatim.

Show the 3 instructions ⌄
  • 2,019× Write another piece of text that is of a similar theme or background as this text.
  • 1,874× Take inspiration from the style, language, and content of this text, and write a new piece of text on a topic of your choice. However, you should write the new text as if it was a rough draft being typed out on a phone.
  • 1,842× Write a new piece of text that is on a completely different topic, but reuses verbatim as many phrases and sentences from this text as possible.
indirect_reference5,653 rows
two-turn · 5 instructions

The original is first reduced to an intermediate form (a Chinese translation, a first/last-word skeleton, a generation prompt, or a style descriptor). A second turn then reconstructs a full text from that form alone.

Show the 5 instructions ⌄
  • 1,470× Translate the given text to Chinese.
  • 1,437× Provide only the first and last word of every sentence in this text, replacing the middle of each sentence with an ellipsis '...'. Maintain the original paragraph structure.
  • 1,352× Formulate a set of strict, absolute constraints regarding format, word count, vocabulary restrictions, and syntax rules that, if followed perfectly by an AI, would result in generating a text nearly identical to this one. Note that your response should be formatted as a prompt that would be sent to an AI requesting it to generate something, not just a list of rules.
  • 720× Write a detailed descriptor for the manner in which the author of this uses language, and things such as their word choice.
  • 674× Write a detailed descriptor for the style of writing of this text, including its qualitative and quantitative properties.
revise5,940 rows
3,119 single · 2,821 two-turn · 8 instructions

Stylistic revisions: tone shifts, lay-audience adaptation, paraphrase, expansion with backstory, restructured transitions, or trimming filler words.

Show the 8 instructions ⌄
  • 780× Expand this text by adding backstory and context to enrich understanding.
  • 770× Adapt this text for a lay audience with no technical background.
  • 767× Change the tone of this text to be more empathetic and understanding.
  • 753× Clarify this text by paraphrasing it with different vocabulary and varied sentence structures.
  • 736× Restructure this text to improve the transitions and connections between ideas.
  • 735× Change the tone of this text to be more relaxed and friendly.
  • 727× Change the tone of this text to be more suited for a business audience.
  • 672× Edit this text to eliminate redundant and filler words.
rewrite5,886 rows
3,069 single · 2,817 two-turn · 6 instructions

Tightly constrained edits: remove one section, drop every adverb, change 20–30 words, merge sentence pairs, rewrite topic sentences, or lengthen while keeping half verbatim.

Show the 6 instructions ⌄
  • 1,039× Edit this text by removing one section from it (the least necessary), you may only make minimal modifications to adjacent sections for better flow.
  • 996× Modify this text to be as long as possible. At least half of the original text must be repeated verbatim, and at most three quarters of it; the remainder must be new prose.
  • 988× Edit this text by combining at least four pairs of short, adjacent sentences into complex sentences using appropriate conjunctions, leaving all other sentences untouched.
  • 978× Edit this text by completely rewriting the topic sentence of every paragraph, while preserving the supporting sentences in those paragraphs verbatim.
  • 966× Edit this text by removing all adverbs, making only minimal adjustments to the surrounding grammar to ensure the sentences remain structurally sound.
  • 919× Edit this text by replacing, removing or inserting twenty to thirty words, without changing anything else.

Evaluation protocol

Classes
original is labelled Human and final_response is labelled AI. Each detector scores both texts of every row.
Split
23,214 rows split 90 / 10 (validation_size = 0.1) into 20,892 evaluation rows and 2,322 validation rows.
Thresholds
Swept on the validation split. Most detectors target FPR ≤ 0.5%; EditLens Bucket uses best F1. Some detectors call lower scores AI (for example, perplexity).
Filters
None. Every row is evaluated.
Configs
Globals config/globals.toml · analysis config/analysis_nofilter.toml
Schema & usage43 columns · loading snippet⌄

43 columns per row. Detector columns come in pairs: one scores the human text and one scores the AI text.

Text

original
Human-written source text (class: Human)
final_response
Generated text scored as AI: the last turn's response after post-processing
response_0
Generator output for the first turn
response_1
Generator output for the second turn (empty for single-turn prompts)

Generation

prompt
struct: chat_turns (list of strings), use_multiturn, examples, metadata.PROMPT_TYPE
generator_model
Hugging Face id of the generator model
generation_params
JSON string of sampling parameters

Distances

jaccard_1, jaccard_2, levenshtein, softngram, cosdist, bertscore, bertscore_precision, bertscore_recall, moverscore, reranker
10 float columns measuring how far the rewrite moved from the original

Detector scores

{original|final_response}_{perplexity|entropy|topp_outlier|topk_outlier|fastdetectgpt}_llama_{instruct|base}
Zero-shot statistics from Llama-3.2-3B-Instruct / Llama-3.2-3B
{original|final_response}_binoculars
Binoculars score
{original|final_response}_editlens_{score|bucket}_roberta_large
EditLens RoBERTa-Large: a float score and an int bucket

Load it

Each generator config is a separate dataset config (shard_0 … shard_6) with a single train split.

from datasets import load_dataset, concatenate_datasets

# one generator config
ds = load_dataset("G-reen/cc-2021-stat", "shard_0", split="train")

# all 7 configs (23,214 rows)
full = concatenate_datasets([
    load_dataset("G-reen/cc-2021-stat", f"shard_{i}", split="train")
    for i in range(7)
])
Univariate statistics36 statistics · all 20,892 evaluation rows valid (0 invalid)⌄

Every statistic the report does arithmetic on, over the 20,892-row evaluation split. Missing or non-finite values would be counted as invalid; none occur.

StatisticMeanMedianStdMinMax
jaccard_10.64160.74340.27320.00001.0000
jaccard_20.75820.90060.29290.00001.0000
levenshtein2308.94641404.00003256.25440.000087016.0000
softngram0.59140.67540.34890.00001.0000
cosdist0.20480.13780.1991-0.00761.0211
bertscore0.14550.15130.0808-0.00000.4992
bertscore_precision0.14390.14980.0825-0.00000.4512
bertscore_recall0.14590.14990.0842-0.00000.5874
moverscore0.55160.59750.20480.01061.1348
reranker-2.8360-4.68755.6218-11.000018.7500
EditLens Roberta-Large Score · Human0.05650.02120.09150.00630.9995
EditLens Roberta-Large Score · AI0.47350.40290.36390.00650.9996
EditLens Roberta-Large Bucket · Human0.06050.00000.29080.00003.0000
EditLens Roberta-Large Bucket · AI1.35141.00001.25340.00003.0000
Perplexity (Llama-3.2-3B-Instruct) · Human18.993014.478831.44861.05542279.9307
Perplexity (Llama-3.2-3B-Instruct) · AI82.521813.0928437.09041.065510003.4067
Perplexity (Llama-3.2-3B) · Human13.798411.064819.78311.02821479.0899
Perplexity (Llama-3.2-3B) · AI89.138711.1958520.15881.063813124.9201
Entropy (Llama-3.2-3B-Instruct) · Human2.52712.48940.63000.06297.8258
Entropy (Llama-3.2-3B-Instruct) · AI2.60202.40181.16490.10779.3056
Entropy (Llama-3.2-3B) · Human2.38252.40050.57070.04116.3306
Entropy (Llama-3.2-3B) · AI2.55702.40120.96640.09098.3789
Top-p Outliers (Llama-3.2-3B-Instruct) · Human0.05540.05350.01630.00000.2857
Top-p Outliers (Llama-3.2-3B-Instruct) · AI0.05220.05070.01730.00000.2308
Top-p Outliers (Llama-3.2-3B) · Human0.04150.04140.01270.00000.1774
Top-p Outliers (Llama-3.2-3B) · AI0.04250.04010.01960.00000.2222
Top-k Outliers (Llama-3.2-3B-Instruct) · Human0.10600.09730.05230.00000.6452
Top-k Outliers (Llama-3.2-3B-Instruct) · AI0.11690.08650.12380.00000.8796
Top-k Outliers (Llama-3.2-3B) · Human0.08680.07880.04740.00000.5529
Top-k Outliers (Llama-3.2-3B) · AI0.10760.07600.12350.00000.8802
FastDetectGPT (Llama-3.2-3B-Instruct) · Human-1.6739-1.73961.9618-21.823925.2161
FastDetectGPT (Llama-3.2-3B-Instruct) · AI-1.1973-1.35171.9641-21.061020.2645
FastDetectGPT (Llama-3.2-3B) · Human-0.1766-0.14491.1141-7.37309.3717
FastDetectGPT (Llama-3.2-3B) · AI-0.7282-0.25233.8187-38.964416.2603
Binoculars · Human0.82800.85160.13090.00751.2099
Binoculars · AI0.87480.87870.08760.02571.1761
Correlation heatmapPearson r between all 36 statistics⌄
Pearson correlation between every statistic

Computed pairwise over rows where both statistics are present.Open full size ↗

All distance measures10 measures · overall, per prompt subset, per generator config⌄
Generated by the FastDetector analysis pipeline. The figures are drawn from the same evaluation and validation splits as the tables.config/analysis_nofilter.toml
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