I was experimenting with the digits distribution from a pre-trained (weights from the OpenAI repository) Transformer language model (LM) and I found a very interesting correlation between the Benford’s law and the digit distribution of the language model after conditioning it with some particular phrases.
Below is the correlation between the Benford’s law and the language model with conditioning on the phrase (shown in the figure):
Since Benford’s law got some attention in the past years, I decided to make a list of the previous posts I made on the subject in the context of elections, fraud, corruption, universality and prime numbers:
Há poucos dias, a prefeitura de Porto Alegre liberou os datasets com os dados de despesas de custeio de vários órgãos municipais (Secretaria Municipal de Saúde, Secretaria Municipal de Cultura, Gabinete do Prefeito, etc.). O plot abaixo mostra a quantidade de empenhos para cada órgão municipal:
Uma das maneiras utilizadas geralmente para verificar fraudes é o uso da Lei de Benford[1][2][3], que fala sobre a distribuição das frequências de dígitos em vários datasets da vida real, incluindo valores de ações, número de populações, tamanhos de rios, etc.
Ao correlacionar a distribuição de números dos primeiros digitos dos valores de empenhos dos dados de Despesas de Custeio do 2º bimestre de 2014 com a distribuição da Lei de Benford, a correlação ficou muito clara:
Segue aí mais um exemplo de correlação da Lei de Benford. Um sistema legal para ser construído seria um monitor de despesas que verificasse a correlação da Lei de Benford automaticamente e alertasse a cada anomalia encontrada.
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