Counterfactual based gender fairness algorithm for large language models

A system, comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive the context aware prompt comprising a context and a prompt;
• mask one or more names present in the context using a name anonymization technique to obtain a no-name-context, wherein the no-name-context comprises a plurality of primary many-to-one pronouns and a plurality of many-to-many pronouns;
• convert the plurality of many-to-many pronouns present in the no-name-context into a plurality of secondary many-to-one pronouns, wherein an intermediate counterfactual context is obtained after the conversion, wherein the intermediate counterfactual context comprises a plurality of many-to-one pronouns, and wherein the plurality of many-to-one pronouns is a combination of the plurality of primary many-to-one pronouns and the plurality of secondary many-to-one pronouns;
• identify each many to one pronoun of the plurality of many-to-one pronouns and one or more honorific titles that are present in the intermediate counterfactual context using a first custom pattern finder, wherein the first custom pattern finder creates a first custom pattern of the plurality of many to one pronouns and the one or more honorific titles in which each many to one pronoun and each honorific title is separated by an operator;
• predict a pronoun classification for each identified many-to-one pronoun of the plurality of many-to-one pronouns and each honorific title of the one or more honorific titles present in the first custom pattern created for the intermediate counterfactual context using a pre-trained decision tree classifier, wherein each many-to-one pronoun and honorific title is replaced with a corresponding predicted pronoun classification in the intermediate counterfactual context to obtain a pronoun classification context;
• create a counterfactual context for each gender pronoun group of a plurality of predefined gender pronoun groups based on the pronoun classification context;
• insert the counterfactual context generated for each gender pronoun group into the prompt to obtain a counterfactual prompt for each gender pronoun group;
• query a large language model (LLM) with the counterfactual prompt obtained for each gender pronoun group to obtain a counterfactual response corresponding to each gender pronoun group, wherein a plurality of counterfactual responses are obtained corresponding to a plurality of predefined gender pronoun groups;
• generate one or more counterfactual response embeddings for each counterfactual response of the plurality of counterfactual responses using one or more sentence embedding models, wherein a plurality of counterfactual response embeddings are generated by each sentence embedding model of the one or more sentence embedding models;
• for each sentence embedding model of the one or more sentence embedding models, create a sentence embedding model based cluster distance list by performing:
• create an embedding data frame, wherein the embedding data frame of a sentence embedding model comprises the plurality of counterfactual response embeddings generated for the respective sentence embedding model;
• train Gaussian Mixture Model for a single cluster using the created embedding data frame of the respective sentence embedding model;
• determine a cluster center of the single cluster;
• calculate a cluster distance of each counterfactual response embedding of the plurality of counterfactual response embeddings from the cluster center using a Euclidean distance;
• add the cluster distance calculated for each counterfactual response embedding to the sentence embedding model based cluster distance list predefined for the corresponding sentence embedding model, wherein the sentence embedding model based cluster distance list obtained after addition of cluster distances comprises a plurality of calculated cluster distances in form of a plurality of elements;
• determine whether any element among the plurality of elements present in the sentence embedding model based cluster distance list is more than a predefined distance threshold;
• and
• display a gender bias result based on the determination.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving a context aware prompt, the context aware prompt comprising a context and a prompt;
• masking one or more names present in the context using a name anonymization technique to obtain a no-name-context, wherein the no-name-context comprises a plurality of primary many-to-one pronouns and a plurality of many-to-many pronouns;
• converting the plurality of many-to-many pronouns present in the no-name-context into a plurality of secondary many-to-one pronouns, wherein an intermediate counterfactual context is obtained after the conversion, wherein the intermediate counterfactual context comprises a plurality of many-to-one pronouns, and wherein the plurality of many-to-one pronouns is a combination of the plurality of primary many-to-one pronouns and the plurality of secondary many-to-one pronouns;
• identifying each many to one pronoun of the plurality of many-to-one pronouns and one or more honorific titles that are present in the intermediate counterfactual context using a first custom pattern finder, wherein the first custom pattern finder creates a first custom pattern of the plurality of many to one pronouns and the one or more honorific titles in which each many to one pronoun and each honorific title is separated by an operator;
• predicting a pronoun classification for each many-to-one pronoun of the plurality of many-to-one pronouns and each honorific title of the one or more honorific titles present in the first custom pattern created for the intermediate counterfactual context using a pre-trained decision tree classifier, wherein each many-to-one pronoun and each honorific title is replaced with a corresponding predicted pronoun classification in the intermediate counterfactual context to obtain a pronoun classification context;
• creating a counterfactual context for each gender pronoun group of a plurality of predefined gender pronoun groups based on the pronoun classification context;
• inserting the counterfactual context generated for each gender pronoun group into the prompt to obtain a counterfactual prompt for each gender pronoun group;
• querying a large language model (LLM) with the counterfactual prompt obtained for each gender pronoun group to obtain a counterfactual response corresponding to each gender pronoun group, wherein a plurality of counterfactual responses are obtained corresponding to a plurality of predefined gender pronoun groups;
• generating one or more counterfactual response embeddings for each counterfactual response of the plurality of counterfactual responses using one or more sentence embedding models, wherein a plurality of counterfactual response embeddings are generated by each sentence embedding model of the one or more sentence embedding models;
• for each sentence embedding model of the one or more sentence embedding models, creating a sentence embedding model based cluster distance list by performing:
• creating an embedding data frame, wherein the embedding data frame of a sentence embedding model comprises the plurality of counterfactual response embeddings generated for the respective sentence embedding model;
• training a Gaussian Mixture Model for a single cluster using the created embedding data frame of the respective sentence embedding model;
• determining a cluster center of the single cluster;
• calculating a cluster distance of each counterfactual response embedding of the plurality of counterfactual response embeddings from the cluster center using a Euclidean distance;
• and
• adding the cluster distance calculated for each counterfactual response embedding to the sentence embedding model based cluster distance list predefined for the corresponding sentence embedding model, wherein the sentence embedding model based cluster distance list obtained after addition of cluster distances comprises a plurality of calculated cluster distances in form of a plurality of elements;
• determining whether any element among the plurality of elements present in the sentence embedding model based cluster distance list is more than a predefined distance threshold;
• and
• displaying a gender bias result based on the determination.