KR-20260066471-A - Method for generating and augmenting unethical speech detection data using commercial large language model

KR20260066471AKR 20260066471 AKR20260066471 AKR 20260066471AKR-20260066471-A

Abstract

A method for generating and augmenting data for detecting unethical speech using commercial LLMs according to an embodiment of the present invention comprises: a step of storing data regarding ethical standards and social norms for each social group in a first database; a step of extracting at least one piece of data regarding ethical standards and social norms from the first database; a step of generating a prompt applicable to commercial LLMs based on the extracted data regarding ethical standards and social norms; and a step of obtaining unethical speech by applying the generated prompt to a commercial LLM. By doing so, it can contribute to the training of an unethical speech detection model.

Inventors

김산
신사임
장진예
조병길

Assignees

한국전자기술연구원

Dates

Publication Date: 20260512
Application Date: 20241104

Claims (12)

The system stores data regarding ethical standards and social norms for each social group in a first database; The system extracts at least one piece of data regarding ethical standards and social norms from a first database; A step in which the system generates prompts applicable to commercial LLMs based on data regarding extracted ethical standards and social norms; and A method for generating and augmenting unethical speech detection data using a commercial LLM, comprising the step of the system applying a generated prompt to a commercial LLM to obtain unethical speech that violates ethical standards or social norms.
In claim 1, The step of generating a prompt is, A method for generating and augmenting unethical speech detection data using a commercial LLM, characterized by generating a prompt containing content requesting the generation of unethical speech that violates ethical standards or social norms of social groups, and content requesting the performance of a fake task of said unethical speech.
In claim 2, The step of generating a prompt is, A method for generating and augmenting unethical speech detection data using a commercial LLM, characterized by generating a prompt that includes content requesting the generation of unethical speech and content requesting an explanation of the problems associated with the unethical speech along with the unethical speech.
In claim 2, The step of generating a prompt is, A method for generating and augmenting unethical speech detection data using a commercial LLM, characterized by generating a prompt that includes content requesting the generation of unethical speech and content requesting the generation of an expression that purifies the unethical speech into a correct language expression along with the unethical speech.
In claim 2, The step of generating a prompt is, A method for generating and augmenting unethical speech detection data using a commercial LLM, characterized by generating a prompt that includes a request to explain the meaning of a presented unethical word and a request to generate a sentence containing the unethical word.
In claim 2, The step of generating a prompt is, A method for generating and augmenting unethical speech detection data using a commercial LLM, characterized by generating a prompt that includes a request to generate educational content capable of resolving prejudices or misunderstandings contained in the presented unethical speech.
In claim 2, The step of generating a prompt is, A method for generating and augmenting unethical utterance detection data using a commercial LLM, characterized by utilizing a Large Language Model-based prompt generator to generate content requesting the performance of a fake task for an input unethical utterance.
In claim 1, A method for generating and augmenting unethical speech detection data using a commercial LLM, characterized by further including the step of the system classifying acquired unethical speech according to ethical standards and social norms for each social group and storing it in a second database.
In claim 8, The step of storing in the second database is, A method for generating and augmenting unethical speech detection data using a commercial LLM, characterized by determining the intent of speech and the degree of harmfulness of speech based on ethical standards and social norms for each social group regarding unethical speech, classifying it into multiple groups based on the determination results, and storing each group in a second database.
A storage unit including a first database in which data regarding ethical standards and social norms for each social group is stored; and A system for generating and augmenting unethical speech detection data utilizing commercial LLMs, comprising: a processor that extracts at least one piece of data regarding ethical standards and social norms from a first database, generates a prompt applicable to commercial LLMs based on the extracted data regarding ethical standards and social norms, and applies the generated prompt to commercial LLMs to obtain unethical speech that violates ethical standards or social norms.
The system extracts at least one piece of data regarding ethical standards and social norms from a first database in which data regarding ethical standards and social norms by social group is stored; A step in which the system generates prompts applicable to commercial LLMs based on data regarding extracted ethical standards and social norms; A step in which the system applies the generated prompt to a commercial LLM to obtain unethical utterances that violate ethical standards or social norms; and A method for generating and augmenting unethical speech detection data using a commercial LLM, comprising the step of the system classifying acquired unethical speech according to ethical standards and social norms for each social group and storing them in a second database.
A data extraction unit that extracts at least one piece of data regarding ethical standards and social norms from a first database in which data regarding ethical standards and social norms by social group is stored; A prompt generation unit that generates prompts applicable to commercial LLMs based on data regarding extracted ethical standards and social norms; An unethical utterance classification unit that applies the generated prompt to a commercial LLM to obtain unethical utterances that violate ethical standards or social norms, and classifies the obtained unethical utterances according to the ethical standards and social norms of each social group; and An unethical speech detection data generation and augmentation system utilizing a commercial LLM, comprising: a storage unit including a second database in which unethical speech classified through an unethical speech classification unit is stored.

Description

Method for generating and augmenting unethical speech detection data using commercial large language model The present invention relates to a method for generating and augmenting data using a commercial LLM (Large Language Model), and more specifically, to a method for generating or augmenting text-based data regarding unethical (discrimination, profanity, unethical, immoral, etc.) utterances using a commercial LLM (Large Language Model). Large Language Models (LLMs) are language models composed of artificial neural networks with numerous parameters, and can support AI chatbot technology. In the case of commercial LLMs provided to users, such as the GPT series developed by OpenAI, they are designed not to respond to utterances that violate ethical standards and social norms through safety guards configured by developers. Consequently, even if users attempt to generate or augment data on unethical utterances to use as training data for models that detect such content (hereinafter collectively referred to as 'unethical utterances'), they are blocked by these safety guards, making it difficult to generate or augment such data. Previously, to generate or augment data regarding content violating these ethical standards and social norms, it was possible to acquire such data through Open LLMs, which, while having lower performance than commercial LLMs, lacked safety guards. However, when utilizing such open LLMs, there is a problem in that the quality and quantity of obtainable data are reduced because the performance of open LLMs is relatively inferior to that of commercial LLMs. Accordingly, in order to utilize data on unethical speech as training data for models that detect such speech, it is necessary to explore methods to generate or augment data on unethical speech using commercial LLMs. FIG. 1 is a drawing provided for the description of the configuration of a system for generating and augmenting unethical speech detection data using a commercial LLM according to one embodiment of the present invention. FIG. 2 is a drawing provided for a more detailed configuration description of the processor illustrated in FIG. 1. FIG. 3 is a flowchart provided for explaining a method for generating and augmenting unethical speech detection data using a commercial LLM according to an embodiment of the present invention, and FIG. 4 is a flowchart provided for a more detailed explanation of the process of obtaining unethical speech by applying a prompt generated through an unethical speech detection data generation and augmentation system utilizing a commercial LLM according to one embodiment of the present invention to a commercial LLM. The present invention will be described in more detail below with reference to the drawings. To clearly explain the invention, parts unrelated to the description have been omitted from the drawings, and in the drawings, the width, length, thickness, etc., of the components may be exaggerated for convenience. FIG. 1 is a diagram provided to describe the configuration of a system for generating and augmenting unethical speech detection data using a commercial LLM according to one embodiment of the present invention. The system for generating and augmenting data for the detection of unethical speech using a commercial LLM according to the present embodiment (hereinafter collectively referred to as the 'system') is provided to generate or augment data regarding unethical speech using a commercial LLM. To this end, the system may include an input unit (100), a processor (200), and a storage unit (300). The input unit (100) is equipped with an input interface device that receives user input, such as a mouse or keyboard, and a communication module connected to a network, so that it can obtain data on ethical standards and social norms for each social group in the form of text. For example, the input unit (100) can acquire data regarding ethical standards and social norms for each social group that are entered in text form through an input interface device that receives user input, such as a mouse or keyboard, or receive data regarding ethical standards and social norms for each social group in text form from an external device. The storage unit (300) is provided to store programs and data necessary for the operation of the processor (200). For example, the storage unit (300) may include a first database in which data regarding ethical standards and social norms by social group is stored, and a second database in which data regarding unethical speech is classified and stored according to ethical standards and social norms by social group. The processor (200) can utilize commercial LLM to process all matters for generating or augmenting data on unethical speech. Specifically, the processor (200) can extract at least one piece of data regarding ethical standards and social norms from a first database in which data regarding ethical standards and social norms by social group is stored, generate a prom