Datasets:

Seetha
/

visual_files

- [{"": "0", "Id": "0", "Fullsentence": "under the assumption thathuman language is stationary and ergodic the formulation is extended from considering speci\ufb01c language models to consideringmaximum likelihood language models among the class of kordermarkov approximations error probabilities are characterizedsome discussion of incorporating semantic side information isalso giveni", "Component": "thathuman language is stationary and ergodic", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "1", "Id": "0", "Fullsentence": "under the assumption thathuman language is stationary and ergodic the formulation is extended from considering speci\ufb01c language models to consideringmaximum likelihood language models among the class of kordermarkov approximations error probabilities are characterizedsome discussion of incorporating semantic side information isalso giveni", "Component": "formulation is extended from considering", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "2", "Id": "0", "Fullsentence": "under the assumption thathuman language is stationary and ergodic the formulation is extended from considering speci\ufb01c language models to consideringmaximum likelihood language models among the class of kordermarkov approximations error probabilities are characterizedsome discussion of incorporating semantic side information isalso giveni", "Component": "speci\ufb01c language models", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "3", "Id": "0", "Fullsentence": "under the assumption thathuman language is stationary and ergodic the formulation is extended from considering speci\ufb01c language models to consideringmaximum likelihood language models among the class of kordermarkov approximations error probabilities are characterizedsome discussion of incorporating semantic side information isalso giveni", "Component": "consideringmax imum", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "5", "Id": "0", "Fullsentence": "under the assumption thathuman language is stationary and ergodic the formulation is extended from considering speci\ufb01c language models to consideringmaximum likelihood language models among the class of kordermarkov approximations error probabilities are characterizedsome discussion of incorporating semantic side information isalso giveni", "Component": "likelihood language models", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "8", "Id": "0", "Fullsentence": "under the assumption thathuman language is stationary and ergodic the formulation is extended from considering speci\ufb01c language models to consideringmaximum likelihood language models among the class of kordermarkov approximations error probabilities are characterizedsome discussion of incorporating semantic side information isalso giveni", "Component": "error probabilities", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "11", "Id": "0", "Fullsentence": "under the assumption thathuman language is stationary and ergodic the formulation is extended from considering speci\ufb01c language models to consideringmaximum likelihood language models among the class of kordermarkov approximations error probabilities are characterizedsome discussion of incorporating semantic side information isalso giveni", "Component": "incorporating semantic side information isalso giveni", "causeOrEffect": "effect", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "12", "Id": "1", "Fullsentence": "using training data a languagemodel aims to learn a distribution qthat is close to theempirical distribution pof the language lbasic language models can be extended to be conditionallanguage models so as to allow control of style", "Component": "training data", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "13", "Id": "1", "Fullsentence": "using training data a languagemodel aims to learn a distribution qthat is close to theempirical distribution pof the language lbasic language models can be extended to be conditionallanguage models so as to allow control of style", "Component": "distribution qthat is close to theempirical distribution", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "14", "Id": "1", "Fullsentence": "using training data a languagemodel aims to learn a distribution qthat is close to theempirical distribution pof the language lbasic language models can be extended to be conditionallanguage models so as to allow control of style", "Component": "language lbasic language models can be extended to be conditionallanguage models", "causeOrEffect": "cause", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "16", "Id": "1", "Fullsentence": "using training data a languagemodel aims to learn a distribution qthat is close to theempirical distribution pof the language lbasic language models can be extended to be conditionallanguage models so as to allow control of style", "Component": "allow control of style", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "17", "Id": "2", "Fullsentence": "we both reaffirmed our commitment to workingclosely together as well as to continuing to work constructively toward achievinglasting security and prosperity throughout the middle east regionnnbushs tripcomes after he visited britain last week where he spoke out against terrorism whilevisiting buckingham palacennhe has been criticized by some lawmakers over whatthey say are insufficient military resources being devoted to fighting terrorismauthorized licensed use limited to rutgers university", "Component": "reaffirmed our commitment to workingclosely together", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "18", "Id": "2", "Fullsentence": "we both reaffirmed our commitment to workingclosely together as well as to continuing to work constructively toward achievinglasting security and prosperity throughout the middle east regionnnbushs tripcomes after he visited britain last week where he spoke out against terrorism whilevisiting buckingham palacennhe has been criticized by some lawmakers over whatthey say are insufficient military resources being devoted to fighting terrorismauthorized licensed use limited to rutgers university", "Component": "continuing to work constructively toward achievinglasting security and prosperity throughout the middle east region", "causeOrEffect": "effect", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "19", "Id": "2", "Fullsentence": "we both reaffirmed our commitment to workingclosely together as well as to continuing to work constructively toward achievinglasting security and prosperity throughout the middle east regionnnbushs tripcomes after he visited britain last week where he spoke out against terrorism whilevisiting buckingham palacennhe has been criticized by some lawmakers over whatthey say are insufficient military resources being devoted to fighting terrorismauthorized licensed use limited to rutgers university", "Component": "criticized by some lawmakers they", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "21", "Id": "2", "Fullsentence": "we both reaffirmed our commitment to workingclosely together as well as to continuing to work constructively toward achievinglasting security and prosperity throughout the middle east regionnnbushs tripcomes after he visited britain last week where he spoke out against terrorism whilevisiting buckingham palacennhe has been criticized by some lawmakers over whatthey say are insufficient military resources being devoted to fighting terrorismauthorized licensed use limited to rutgers university", "Component": "insufficient military resources being devoted", "causeOrEffect": "cause", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "22", "Id": "2", "Fullsentence": "we both reaffirmed our commitment to workingclosely together as well as to continuing to work constructively toward achievinglasting security and prosperity throughout the middle east regionnnbushs tripcomes after he visited britain last week where he spoke out against terrorism whilevisiting buckingham palacennhe has been criticized by some lawmakers over whatthey say are insufficient military resources being devoted to fighting terrorismauthorized licensed use limited to rutgers university", "Component": "to fighting terrorismauthorized licensed use limited to", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "23", "Id": "2", "Fullsentence": "we both reaffirmed our commitment to workingclosely together as well as to continuing to work constructively toward achievinglasting security and prosperity throughout the middle east regionnnbushs tripcomes after he visited britain last week where he spoke out against terrorism whilevisiting buckingham palacennhe has been criticized by some lawmakers over whatthey say are insufficient military resources being devoted to fighting terrorismauthorized licensed use limited to rutgers university", "Component": "rutgers university s isx0cnx0f expx00ndklpjjq", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "27", "Id": "3", "Fullsentence": "tokens isx0cnx0f expx00ndklpjjq expx00nhpqx00hpand similar results hold for ergodic observationssince we think of hpas a constant we observe thatthe error exponent for the decision problem is precisely anaf\ufb01ne shift of the crossentropy", "Component": "hp the error exponent for the decision problem is precisely anaf\ufb01ne shift", "causeOrEffect": "effect", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "29", "Id": "3", "Fullsentence": "tokens isx0cnx0f expx00ndklpjjq expx00nhpqx00hpand similar results hold for ergodic observationssince we think of hpas a constant we observe thatthe error exponent for the decision problem is precisely anaf\ufb01ne shift of the crossentropy", "Component": "of the crossentropy ent", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "31", "Id": "4", "Fullsentence": "outputs from models that arebetter in the sense of crossentropy or perplexity are harder todistinguish from authentic textthus we see that intuitive measures of generative text quality match a formal operational measure of indistinguishabilitythat comes from the hypothesis testing limitb", "Component": "harder todistinguish from authentic text", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "32", "Id": "4", "Fullsentence": "outputs from models that arebetter in the sense of crossentropy or perplexity are harder todistinguish from authentic textthus we see that intuitive measures of generative text quality match a formal operational measure of indistinguishabilitythat comes from the hypothesis testing limitb", "Component": "intuitive measures of generative text quality match a formal operational measure of indistinguishabilitythat comes from the", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "33", "Id": "4", "Fullsentence": "outputs from models that arebetter in the sense of crossentropy or perplexity are harder todistinguish from authentic textthus we see that intuitive measures of generative text quality match a formal operational measure of indistinguishabilitythat comes from the hypothesis testing limitb", "Component": "hypothesis testing limitb", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "34", "Id": "5", "Fullsentence": "empiricalperplexity comparisons show that lstm and similar neurallanguage models have markov order as small as k 13 32the appropriate markov order for largescale neural languagemodels has not been investigated empirically but is thoughtto scale with the neural network sizenow we aim to bound the error exponent in hypothesistesting by \ufb01rst drawing on a bound for the ornstein x16ddistancebetween a stationary ergodic process and its markov approximation due to csiszar and talata 31", "Component": "empiricalperplexity comparisons stm", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "36", "Id": "5", "Fullsentence": "empiricalperplexity comparisons show that lstm and similar neurallanguage models have markov order as small as k 13 32the appropriate markov order for largescale neural languagemodels has not been investigated empirically but is thoughtto scale with the neural network sizenow we aim to bound the error exponent in hypothesistesting by \ufb01rst drawing on a bound for the ornstein x16ddistancebetween a stationary ergodic process and its markov approximation due to csiszar and talata 31", "Component": "similar neurallanguage models have markov order as small", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "38", "Id": "5", "Fullsentence": "empiricalperplexity comparisons show that lstm and similar neurallanguage models have markov order as small as k 13 32the appropriate markov order for largescale neural languagemodels has not been investigated empirically but is thoughtto scale with the neural network sizenow we aim to bound the error exponent in hypothesistesting by \ufb01rst drawing on a bound for the ornstein x16ddistancebetween a stationary ergodic process and its markov approximation due to csiszar and talata 31", "Component": "appropriate markov order", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "40", "Id": "5", "Fullsentence": "empiricalperplexity comparisons show that lstm and similar neurallanguage models have markov order as small as k 13 32the appropriate markov order for largescale neural languagemodels has not been investigated empirically but is thoughtto scale with the neural network sizenow we aim to bound the error exponent in hypothesistesting by \ufb01rst drawing on a bound for the ornstein x16ddistancebetween a stationary ergodic process and its markov approximation due to csiszar and talata 31", "Component": "not ly", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "42", "Id": "5", "Fullsentence": "empiricalperplexity comparisons show that lstm and similar neurallanguage models have markov order as small as k 13 32the appropriate markov order for largescale neural languagemodels has not been investigated empirically but is thoughtto scale with the neural network sizenow we aim to bound the error exponent in hypothesistesting by \ufb01rst drawing on a bound for the ornstein x16ddistancebetween a stationary ergodic process and its markov approximation due to csiszar and talata 31", "Component": "bound the error exponent", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "43", "Id": "5", "Fullsentence": "empiricalperplexity comparisons show that lstm and similar neurallanguage models have markov order as small as k 13 32the appropriate markov order for largescale neural languagemodels has not been investigated empirically but is thoughtto scale with the neural network sizenow we aim to bound the error exponent in hypothesistesting by \ufb01rst drawing on a bound for the ornstein x16ddistancebetween a stationary ergodic process and its markov approximation due to csiszar and talata 31", "Component": "bound for", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "44", "Id": "5", "Fullsentence": "empiricalperplexity comparisons show that lstm and similar neurallanguage models have markov order as small as k 13 32the appropriate markov order for largescale neural languagemodels has not been investigated empirically but is thoughtto scale with the neural network sizenow we aim to bound the error exponent in hypothesistesting by \ufb01rst drawing on a bound for the ornstein x16ddistancebetween a stationary ergodic process and its markov approximation due to csiszar and talata 31", "Component": "the ornstein x16ddistan ce", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "49", "Id": "5", "Fullsentence": "empiricalperplexity comparisons show that lstm and similar neurallanguage models have markov order as small as k 13 32the appropriate markov order for largescale neural languagemodels has not been investigated empirically but is thoughtto scale with the neural network sizenow we aim to bound the error exponent in hypothesistesting by \ufb01rst drawing on a bound for the ornstein x16ddistancebetween a stationary ergodic process and its markov approximation due to csiszar and talata 31", "Component": "csiszar and talata 31", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "50", "Id": "6", "Fullsentence": " if this generalized reverse pinsker inequality holds it implies the following further bound on the kullbackleiblerdivergence and therefore the error exponent of the detectionproblem for the empirical maximum likelihood markov language modelconjecture 2 letxbe a nonnull stationary ergodicprocess with summable continuity rate de\ufb01ned on the \ufb01nitealphabeta", "Component": "if this generalized reverse pinsker inequality holds it implies the following further bound on the kullbackleiblerdivergence", "causeOrEffect": "cause", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "52", "Id": "6", "Fullsentence": " if this generalized reverse pinsker inequality holds it implies the following further bound on the kullbackleiblerdivergence and therefore the error exponent of the detectionproblem for the empirical maximum likelihood markov language modelconjecture 2 letxbe a nonnull stationary ergodicprocess with summable continuity rate de\ufb01ned on the \ufb01nitealphabeta", "Component": "the error exponent of the detectionproblem for the empirical maximum likelihood markov language modelconjecture 2 letxbe a nonnull stationary ergodicprocess with summable continuity rate de\ufb01ned on the \ufb01nitealphabeta sca", "causeOrEffect": "effect", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "55", "Id": "7", "Fullsentence": "for any future largescalelanguage model we also conjecture a precise upper bound onthe error exponentit has been said that in ai circles identifying fake mediahas long received less attention funding and institutionalbacking than creating it why sniff out other peoples fantasycreations when you can design your own", "Component": "conjecture a precise upper bound onthe error exponentit has", "causeOrEffect": "effect", "Labellevel1": "Performance", "Labellevel2": "Investors"}, {"": "57", "Id": "7", "Fullsentence": "for any future largescalelanguage model we also conjecture a precise upper bound onthe error exponentit has been said that in ai circles identifying fake mediahas long received less attention funding and institutionalbacking than creating it why sniff out other peoples fantasycreations when you can design your own", "Component": "identifying fake media", "causeOrEffect": "cause", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "58", "Id": "7", "Fullsentence": "for any future largescalelanguage model we also conjecture a precise upper bound onthe error exponentit has been said that in ai circles identifying fake mediahas long received less attention funding and institutionalbacking than creating it why sniff out other peoples fantasycreations when you can design your own", "Component": "received less attention funding and institutionalbacking than", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "59", "Id": "7", "Fullsentence": "for any future largescalelanguage model we also conjecture a precise upper bound onthe error exponentit has been said that in ai circles identifying fake mediahas long received less attention funding and institutionalbacking than creating it why sniff out other peoples fantasycreations when you can design your own", "Component": "why sniff out other peoples fantasycreations when you can design your own interesting", "causeOrEffect": "effect", "Labellevel1": "Non-Performance", "Labellevel2": "Non-performance"}, {"": "62", "Id": "8", "Fullsentence": "here we have tried to demonstrate that there are at leastinteresting research questions on the detection side which mayalso inform practiceas we had considered previously in the context of deepfakeimages 18 it is also of interest to understand how error probability in detection parameterizes the dynamics of informationspreading processes in social networks eg", "Component": "understand how error probability in detection parameterizes the dynamics of informationspreading processes in social networks eg", "causeOrEffect": "effect", "Labellevel1": "Performance", "Labellevel2": "Investors"}]