Five-time winner of Best Education Podcast in the Podcast Awards. Grammar Girl provides short, friendly tips to improve your writing and feed your love of the English language. Whether English is your first language or your second language, these grammar, punctuation, style, and business tips will make you a better and more successful writer. Grammar Girl is a Quick and Dirty Tips podcast.
…
continue reading
Indhold leveret af The Nonlinear Fund. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af The Nonlinear Fund eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.
Player FM - Podcast-app
Gå offline med appen Player FM !
Gå offline med appen Player FM !
LW - Glitch Token Catalog - (Almost) a Full Clear by Lao Mein
MP3•Episode hjem
Manage episode 441207474 series 2997284
Indhold leveret af The Nonlinear Fund. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af The Nonlinear Fund eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Glitch Token Catalog - (Almost) a Full Clear, published by Lao Mein on September 22, 2024 on LessWrong.
This is a collection of every unidentified GPT2 glitch token listed in the third glitch token archaeology post. I was able to find the source of every single one, except for "?????-" and "?????-?????-"[1]. Please tell me if I missed one, or you've discovered one and don't understand where it came from. This isn't meant to be a well-written analysis, just a quick repository of my glitch-hunting observations.
I plan on writing up and categorizing all of these in greater detail in future posts. The first of which is here.
I used OpenWebText, a recreation of GPT2's training data, for all experiments in this post. I tokenized every .gz file in the archive and made a boolean Numpy array of each tokens that was present at least once. This allowed me to quickly identify infrequent tokens in the dataset and pull up the textual context with regular expressions. If there was an issue with overlap, I used a tokenizer-based extraction instead. All data/code available upon request.
The leftmost column is token id, the middle is the token string, and the right column is # of files the token was present in (out of 20610). GPT2 has 50256 total tokens.
GPT2 tokens with the lowest frequency in OpenWebText
30898 'embedreportprint' 0 33434 ' 士' 0 43453 ' SolidGoldMagikarp' 0 1849 '\xa0' 0 47654 ' \xa0\xa0' 0 50009 ' strutConnector' 0 36173 ' RandomRedditor' 0 214 '\x1a' 0 42424 'DragonMagazine' 0 180 ' ' 0 187 ' ' 0 186 ' ' 0 30213 ' externalToEVAOnly' 0 30212 ' externalToEVA' 0 30211 ' guiIcon' 0 185 ' ' 0 30210 ' guiActiveUnfocused' 0 30209 ' unfocusedRange' 0 184 ' ' 0 30202 ' guiName' 0 183 ' ' 0 30905 'rawdownload' 0 39906 'EStream' 0 33454 '龍喚士' 0 42586 ' srfN' 0 25992 ' 裏覚醒' 0 43065 '
srfAttach' 0 11504 ' \xa0 \xa0' 0 39172 '\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0' 0 40240 'oreAndOnline' 0 40241 'InstoreAndOnline' 0 33477 '\xa0\xa0\xa0' 0 36174 ' RandomRedditorWithNo' 0 37574 'StreamerBot' 0 46600 ' Adinida' 0 182 ' ' 0 29372 ' guiActiveUn' 0 43177 'EStreamFrame' 0 22686 ' \xa0 \xa0 \xa0 \xa0' 0 23282 ' davidjl' 0 47571 ' DevOnline' 0 39752 'quickShip' 0 44320 '\n\xa0' 0 8828 '\xa0\xa0\xa0\xa0' 0 39820 '龍 ' 0 39821 '龍契士' 0 28666 'PsyNetMessage' 0 35207
' attRot' 0 181 ' ' 0 18472 ' guiActive' 0 179 ' ' 0 17811 '\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0' 0 20174 ' 裏 ' 0 212 '\x18' 0 211 '\x17' 0 210 '\x16' 0 209 '\x15' 0 208 '\x14' 0 31666 '?????-?????-' 0 207 '\x13' 0 206 '\x12' 0 213 '\x19' 0 205 '\x11' 0 203 '\x0f' 0 202 '\x0e' 0 31957 'cffffcc' 0 200 '\x0c' 0 199 '\x0b' 0 197 '\t' 0 196 '\x08' 0 195 '\x07' 0 194 '\x06' 0 193 '\x05' 0 204 '\x10' 0 45545 ' サーティワン' 0 201 '\r' 0 216 '\x1c' 0 37842 ' partName' 0 45706 ' \xa0 \xa0 \xa0 \xa0 \xa0 \xa0 \xa0
\xa0' 0 124 ' ' 0 125 ' ' 0 178 ' ' 0 41380 'natureconservancy' 0 41383 'assetsadobe' 0 177 ' ' 0 215 '\x1b' 0 41551 'Downloadha' 0 4603 '\xa0\xa0' 0 42202 'GoldMagikarp' 0 42089 ' TheNitrome' 0 217 '\x1d' 0 218 '\x1e' 0 42090 ' TheNitromeFan' 0 192 '\x04' 0 191 '\x03' 0 219 '\x1f' 0 189 '\x01' 0 45544 ' サーティ' 0 5624 ' \xa0' 0 190 '\x02' 0 40242 'BuyableInstoreAndOnline' 1 36935 ' dstg' 1 36940 ' istg' 1 45003 ' SetTextColor' 1 30897 'reportprint' 1 39757 'channelAvailability' 1 39756
'inventoryQuantity' 1 39755 'isSpecialOrderable' 1 39811 'soDeliveryDate' 1 39753 'quickShipAvailable' 1 39714 'isSpecial' 1 47198 'ItemTracker' 1 17900 ' Dragonbound' 1 45392 'dayName' 1 37579 'TPPStreamerBot' 1 31573 'ActionCode' 2 25193 'NetMessage' 2 39749 'DeliveryDate' 2 30208 ' externalTo' 2 43569 'ÍÍ' 2 34027 ' actionGroup' 2 34504 ' 裏 ' 2 39446 ' SetFontSize' 2 30899 'cloneembedreportprint' 2 32047 ' "$:/' 3 39803 'soType' 3 39177 'ItemThumbnailImage' 3 49781 'EngineDebug' 3 25658
'?????-' 3 33813 '=~=~' 3 48396 'ÛÛ' 3 34206 ...
…
continue reading
This is a collection of every unidentified GPT2 glitch token listed in the third glitch token archaeology post. I was able to find the source of every single one, except for "?????-" and "?????-?????-"[1]. Please tell me if I missed one, or you've discovered one and don't understand where it came from. This isn't meant to be a well-written analysis, just a quick repository of my glitch-hunting observations.
I plan on writing up and categorizing all of these in greater detail in future posts. The first of which is here.
I used OpenWebText, a recreation of GPT2's training data, for all experiments in this post. I tokenized every .gz file in the archive and made a boolean Numpy array of each tokens that was present at least once. This allowed me to quickly identify infrequent tokens in the dataset and pull up the textual context with regular expressions. If there was an issue with overlap, I used a tokenizer-based extraction instead. All data/code available upon request.
The leftmost column is token id, the middle is the token string, and the right column is # of files the token was present in (out of 20610). GPT2 has 50256 total tokens.
GPT2 tokens with the lowest frequency in OpenWebText
30898 'embedreportprint' 0 33434 ' 士' 0 43453 ' SolidGoldMagikarp' 0 1849 '\xa0' 0 47654 ' \xa0\xa0' 0 50009 ' strutConnector' 0 36173 ' RandomRedditor' 0 214 '\x1a' 0 42424 'DragonMagazine' 0 180 ' ' 0 187 ' ' 0 186 ' ' 0 30213 ' externalToEVAOnly' 0 30212 ' externalToEVA' 0 30211 ' guiIcon' 0 185 ' ' 0 30210 ' guiActiveUnfocused' 0 30209 ' unfocusedRange' 0 184 ' ' 0 30202 ' guiName' 0 183 ' ' 0 30905 'rawdownload' 0 39906 'EStream' 0 33454 '龍喚士' 0 42586 ' srfN' 0 25992 ' 裏覚醒' 0 43065 '
srfAttach' 0 11504 ' \xa0 \xa0' 0 39172 '\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0' 0 40240 'oreAndOnline' 0 40241 'InstoreAndOnline' 0 33477 '\xa0\xa0\xa0' 0 36174 ' RandomRedditorWithNo' 0 37574 'StreamerBot' 0 46600 ' Adinida' 0 182 ' ' 0 29372 ' guiActiveUn' 0 43177 'EStreamFrame' 0 22686 ' \xa0 \xa0 \xa0 \xa0' 0 23282 ' davidjl' 0 47571 ' DevOnline' 0 39752 'quickShip' 0 44320 '\n\xa0' 0 8828 '\xa0\xa0\xa0\xa0' 0 39820 '龍 ' 0 39821 '龍契士' 0 28666 'PsyNetMessage' 0 35207
' attRot' 0 181 ' ' 0 18472 ' guiActive' 0 179 ' ' 0 17811 '\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0' 0 20174 ' 裏 ' 0 212 '\x18' 0 211 '\x17' 0 210 '\x16' 0 209 '\x15' 0 208 '\x14' 0 31666 '?????-?????-' 0 207 '\x13' 0 206 '\x12' 0 213 '\x19' 0 205 '\x11' 0 203 '\x0f' 0 202 '\x0e' 0 31957 'cffffcc' 0 200 '\x0c' 0 199 '\x0b' 0 197 '\t' 0 196 '\x08' 0 195 '\x07' 0 194 '\x06' 0 193 '\x05' 0 204 '\x10' 0 45545 ' サーティワン' 0 201 '\r' 0 216 '\x1c' 0 37842 ' partName' 0 45706 ' \xa0 \xa0 \xa0 \xa0 \xa0 \xa0 \xa0
\xa0' 0 124 ' ' 0 125 ' ' 0 178 ' ' 0 41380 'natureconservancy' 0 41383 'assetsadobe' 0 177 ' ' 0 215 '\x1b' 0 41551 'Downloadha' 0 4603 '\xa0\xa0' 0 42202 'GoldMagikarp' 0 42089 ' TheNitrome' 0 217 '\x1d' 0 218 '\x1e' 0 42090 ' TheNitromeFan' 0 192 '\x04' 0 191 '\x03' 0 219 '\x1f' 0 189 '\x01' 0 45544 ' サーティ' 0 5624 ' \xa0' 0 190 '\x02' 0 40242 'BuyableInstoreAndOnline' 1 36935 ' dstg' 1 36940 ' istg' 1 45003 ' SetTextColor' 1 30897 'reportprint' 1 39757 'channelAvailability' 1 39756
'inventoryQuantity' 1 39755 'isSpecialOrderable' 1 39811 'soDeliveryDate' 1 39753 'quickShipAvailable' 1 39714 'isSpecial' 1 47198 'ItemTracker' 1 17900 ' Dragonbound' 1 45392 'dayName' 1 37579 'TPPStreamerBot' 1 31573 'ActionCode' 2 25193 'NetMessage' 2 39749 'DeliveryDate' 2 30208 ' externalTo' 2 43569 'ÍÍ' 2 34027 ' actionGroup' 2 34504 ' 裏 ' 2 39446 ' SetFontSize' 2 30899 'cloneembedreportprint' 2 32047 ' "$:/' 3 39803 'soType' 3 39177 'ItemThumbnailImage' 3 49781 'EngineDebug' 3 25658
'?????-' 3 33813 '=~=~' 3 48396 'ÛÛ' 3 34206 ...
2447 episoder
MP3•Episode hjem
Manage episode 441207474 series 2997284
Indhold leveret af The Nonlinear Fund. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af The Nonlinear Fund eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Glitch Token Catalog - (Almost) a Full Clear, published by Lao Mein on September 22, 2024 on LessWrong.
This is a collection of every unidentified GPT2 glitch token listed in the third glitch token archaeology post. I was able to find the source of every single one, except for "?????-" and "?????-?????-"[1]. Please tell me if I missed one, or you've discovered one and don't understand where it came from. This isn't meant to be a well-written analysis, just a quick repository of my glitch-hunting observations.
I plan on writing up and categorizing all of these in greater detail in future posts. The first of which is here.
I used OpenWebText, a recreation of GPT2's training data, for all experiments in this post. I tokenized every .gz file in the archive and made a boolean Numpy array of each tokens that was present at least once. This allowed me to quickly identify infrequent tokens in the dataset and pull up the textual context with regular expressions. If there was an issue with overlap, I used a tokenizer-based extraction instead. All data/code available upon request.
The leftmost column is token id, the middle is the token string, and the right column is # of files the token was present in (out of 20610). GPT2 has 50256 total tokens.
GPT2 tokens with the lowest frequency in OpenWebText
30898 'embedreportprint' 0 33434 ' 士' 0 43453 ' SolidGoldMagikarp' 0 1849 '\xa0' 0 47654 ' \xa0\xa0' 0 50009 ' strutConnector' 0 36173 ' RandomRedditor' 0 214 '\x1a' 0 42424 'DragonMagazine' 0 180 ' ' 0 187 ' ' 0 186 ' ' 0 30213 ' externalToEVAOnly' 0 30212 ' externalToEVA' 0 30211 ' guiIcon' 0 185 ' ' 0 30210 ' guiActiveUnfocused' 0 30209 ' unfocusedRange' 0 184 ' ' 0 30202 ' guiName' 0 183 ' ' 0 30905 'rawdownload' 0 39906 'EStream' 0 33454 '龍喚士' 0 42586 ' srfN' 0 25992 ' 裏覚醒' 0 43065 '
srfAttach' 0 11504 ' \xa0 \xa0' 0 39172 '\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0' 0 40240 'oreAndOnline' 0 40241 'InstoreAndOnline' 0 33477 '\xa0\xa0\xa0' 0 36174 ' RandomRedditorWithNo' 0 37574 'StreamerBot' 0 46600 ' Adinida' 0 182 ' ' 0 29372 ' guiActiveUn' 0 43177 'EStreamFrame' 0 22686 ' \xa0 \xa0 \xa0 \xa0' 0 23282 ' davidjl' 0 47571 ' DevOnline' 0 39752 'quickShip' 0 44320 '\n\xa0' 0 8828 '\xa0\xa0\xa0\xa0' 0 39820 '龍 ' 0 39821 '龍契士' 0 28666 'PsyNetMessage' 0 35207
' attRot' 0 181 ' ' 0 18472 ' guiActive' 0 179 ' ' 0 17811 '\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0' 0 20174 ' 裏 ' 0 212 '\x18' 0 211 '\x17' 0 210 '\x16' 0 209 '\x15' 0 208 '\x14' 0 31666 '?????-?????-' 0 207 '\x13' 0 206 '\x12' 0 213 '\x19' 0 205 '\x11' 0 203 '\x0f' 0 202 '\x0e' 0 31957 'cffffcc' 0 200 '\x0c' 0 199 '\x0b' 0 197 '\t' 0 196 '\x08' 0 195 '\x07' 0 194 '\x06' 0 193 '\x05' 0 204 '\x10' 0 45545 ' サーティワン' 0 201 '\r' 0 216 '\x1c' 0 37842 ' partName' 0 45706 ' \xa0 \xa0 \xa0 \xa0 \xa0 \xa0 \xa0
\xa0' 0 124 ' ' 0 125 ' ' 0 178 ' ' 0 41380 'natureconservancy' 0 41383 'assetsadobe' 0 177 ' ' 0 215 '\x1b' 0 41551 'Downloadha' 0 4603 '\xa0\xa0' 0 42202 'GoldMagikarp' 0 42089 ' TheNitrome' 0 217 '\x1d' 0 218 '\x1e' 0 42090 ' TheNitromeFan' 0 192 '\x04' 0 191 '\x03' 0 219 '\x1f' 0 189 '\x01' 0 45544 ' サーティ' 0 5624 ' \xa0' 0 190 '\x02' 0 40242 'BuyableInstoreAndOnline' 1 36935 ' dstg' 1 36940 ' istg' 1 45003 ' SetTextColor' 1 30897 'reportprint' 1 39757 'channelAvailability' 1 39756
'inventoryQuantity' 1 39755 'isSpecialOrderable' 1 39811 'soDeliveryDate' 1 39753 'quickShipAvailable' 1 39714 'isSpecial' 1 47198 'ItemTracker' 1 17900 ' Dragonbound' 1 45392 'dayName' 1 37579 'TPPStreamerBot' 1 31573 'ActionCode' 2 25193 'NetMessage' 2 39749 'DeliveryDate' 2 30208 ' externalTo' 2 43569 'ÍÍ' 2 34027 ' actionGroup' 2 34504 ' 裏 ' 2 39446 ' SetFontSize' 2 30899 'cloneembedreportprint' 2 32047 ' "$:/' 3 39803 'soType' 3 39177 'ItemThumbnailImage' 3 49781 'EngineDebug' 3 25658
'?????-' 3 33813 '=~=~' 3 48396 'ÛÛ' 3 34206 ...
…
continue reading
This is a collection of every unidentified GPT2 glitch token listed in the third glitch token archaeology post. I was able to find the source of every single one, except for "?????-" and "?????-?????-"[1]. Please tell me if I missed one, or you've discovered one and don't understand where it came from. This isn't meant to be a well-written analysis, just a quick repository of my glitch-hunting observations.
I plan on writing up and categorizing all of these in greater detail in future posts. The first of which is here.
I used OpenWebText, a recreation of GPT2's training data, for all experiments in this post. I tokenized every .gz file in the archive and made a boolean Numpy array of each tokens that was present at least once. This allowed me to quickly identify infrequent tokens in the dataset and pull up the textual context with regular expressions. If there was an issue with overlap, I used a tokenizer-based extraction instead. All data/code available upon request.
The leftmost column is token id, the middle is the token string, and the right column is # of files the token was present in (out of 20610). GPT2 has 50256 total tokens.
GPT2 tokens with the lowest frequency in OpenWebText
30898 'embedreportprint' 0 33434 ' 士' 0 43453 ' SolidGoldMagikarp' 0 1849 '\xa0' 0 47654 ' \xa0\xa0' 0 50009 ' strutConnector' 0 36173 ' RandomRedditor' 0 214 '\x1a' 0 42424 'DragonMagazine' 0 180 ' ' 0 187 ' ' 0 186 ' ' 0 30213 ' externalToEVAOnly' 0 30212 ' externalToEVA' 0 30211 ' guiIcon' 0 185 ' ' 0 30210 ' guiActiveUnfocused' 0 30209 ' unfocusedRange' 0 184 ' ' 0 30202 ' guiName' 0 183 ' ' 0 30905 'rawdownload' 0 39906 'EStream' 0 33454 '龍喚士' 0 42586 ' srfN' 0 25992 ' 裏覚醒' 0 43065 '
srfAttach' 0 11504 ' \xa0 \xa0' 0 39172 '\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0' 0 40240 'oreAndOnline' 0 40241 'InstoreAndOnline' 0 33477 '\xa0\xa0\xa0' 0 36174 ' RandomRedditorWithNo' 0 37574 'StreamerBot' 0 46600 ' Adinida' 0 182 ' ' 0 29372 ' guiActiveUn' 0 43177 'EStreamFrame' 0 22686 ' \xa0 \xa0 \xa0 \xa0' 0 23282 ' davidjl' 0 47571 ' DevOnline' 0 39752 'quickShip' 0 44320 '\n\xa0' 0 8828 '\xa0\xa0\xa0\xa0' 0 39820 '龍 ' 0 39821 '龍契士' 0 28666 'PsyNetMessage' 0 35207
' attRot' 0 181 ' ' 0 18472 ' guiActive' 0 179 ' ' 0 17811 '\xa0\xa0\xa0\xa0\xa0\xa0\xa0\xa0' 0 20174 ' 裏 ' 0 212 '\x18' 0 211 '\x17' 0 210 '\x16' 0 209 '\x15' 0 208 '\x14' 0 31666 '?????-?????-' 0 207 '\x13' 0 206 '\x12' 0 213 '\x19' 0 205 '\x11' 0 203 '\x0f' 0 202 '\x0e' 0 31957 'cffffcc' 0 200 '\x0c' 0 199 '\x0b' 0 197 '\t' 0 196 '\x08' 0 195 '\x07' 0 194 '\x06' 0 193 '\x05' 0 204 '\x10' 0 45545 ' サーティワン' 0 201 '\r' 0 216 '\x1c' 0 37842 ' partName' 0 45706 ' \xa0 \xa0 \xa0 \xa0 \xa0 \xa0 \xa0
\xa0' 0 124 ' ' 0 125 ' ' 0 178 ' ' 0 41380 'natureconservancy' 0 41383 'assetsadobe' 0 177 ' ' 0 215 '\x1b' 0 41551 'Downloadha' 0 4603 '\xa0\xa0' 0 42202 'GoldMagikarp' 0 42089 ' TheNitrome' 0 217 '\x1d' 0 218 '\x1e' 0 42090 ' TheNitromeFan' 0 192 '\x04' 0 191 '\x03' 0 219 '\x1f' 0 189 '\x01' 0 45544 ' サーティ' 0 5624 ' \xa0' 0 190 '\x02' 0 40242 'BuyableInstoreAndOnline' 1 36935 ' dstg' 1 36940 ' istg' 1 45003 ' SetTextColor' 1 30897 'reportprint' 1 39757 'channelAvailability' 1 39756
'inventoryQuantity' 1 39755 'isSpecialOrderable' 1 39811 'soDeliveryDate' 1 39753 'quickShipAvailable' 1 39714 'isSpecial' 1 47198 'ItemTracker' 1 17900 ' Dragonbound' 1 45392 'dayName' 1 37579 'TPPStreamerBot' 1 31573 'ActionCode' 2 25193 'NetMessage' 2 39749 'DeliveryDate' 2 30208 ' externalTo' 2 43569 'ÍÍ' 2 34027 ' actionGroup' 2 34504 ' 裏 ' 2 39446 ' SetFontSize' 2 30899 'cloneembedreportprint' 2 32047 ' "$:/' 3 39803 'soType' 3 39177 'ItemThumbnailImage' 3 49781 'EngineDebug' 3 25658
'?????-' 3 33813 '=~=~' 3 48396 'ÛÛ' 3 34206 ...
2447 episoder
Alle episoder
×Velkommen til Player FM!
Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.