Methods and systems for identifying and mitigating negative bias in large language model

A method for mitigating negative bias in a large language model (LLM), performed by a computing system, comprising:

• acquiring a first dataset including samples, each of the samples including a question and a label for the question;
• identifying probing samples from among the samples included in the first dataset, the probing samples being samples whose labels match answers obtained by inputting corresponding questions to the LLM;
• converting each of the probing samples into a positive sample;
• generating a second dataset including positive samples corresponding to the probing samples;
• calculating first negative bias attention scores (NASs) of a plurality of attention heads of the LLM for the second dataset;
• selecting one or more attention heads from among the plurality of attention heads of the LLM using the first NASs;
• and
• performing a fine-tuning process on the selected one or more attention heads.

A system for identifying negative bias, comprising:

• at least one processor;
• and
• at least one memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations,
• wherein the operations comprise:
• converting each of a first plurality of samples, from among samples included in a first dataset, into a positive sample including a positive answer, each of the samples including a question and a label for the question;
• converting each of a second plurality of samples, which are remaining samples excluding the first plurality of samples, into a negative sample including a negative answer;
• generating a second dataset including positive samples corresponding to the first plurality of samples and negative samples corresponding to the second plurality of samples;
• inputting the positive samples and the negative samples included in the second dataset into a first large language model (LLM), the first LLM being a language model configured to generate an answer to the question;
• and
• identifying negative bias of the first LLM using information output from the first LLM.

A system for mitigating negative bias, comprising:

• at least one processor;
• and
• at least one memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations,
• wherein the operations comprise:
• acquiring a first dataset including samples, each of the samples including a question and a label for the question;
• identifying probing samples from among the samples included in the first dataset, the probing samples being samples whose labels match answers obtained by inputting corresponding questions to a large language model (LLM);
• converting each of the probing samples into a positive sample;
• generating a second dataset including positive samples corresponding to the probing samples;
• calculating first negative bias attention scores (NASs) of a plurality of attention heads of the LLM for the second dataset;
• selecting one or more attention heads from among the plurality of attention heads of the LLM using the first NASs;
• and performing a fine-tuning process on the selected one or more attention heads.