WO-2026095094-A1 - ELECTRONIC DEVICE, METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM FOR GENERATING DATA SET FOR TRAINING MODEL

Abstract

This electronic device may comprise a memory for storing instructions, and at least one processor. The instructions, when executed by the at least one processor, may cause the electronic device to: acquire first text for a prompt by providing the prompt to a first language model; acquire, with respect to the prompt, second text of less relevance than that between the prompt and the first text by using a second language model different from the first language model; and generate a data set in which the prompt, the first text, and the second text are combined.

Inventors

• KIM, DAEYOUNG
• PARK, JUNSOO
• REN, Meiying
• JWA, Seungyeon
• CHOI, SANGHYUK

Assignees

• 주식회사 엔씨소프트

Dates

Publication Date

20260507

Application Date

20241029

Claims (20)

1. In electronic devices, Memory comprising one or more storage media and storing instructions; and It includes at least one processor comprising processing circuitry, and When the above instructions are executed individually or collectively by the at least one processor, By providing a prompt to a first language model, a first text for the prompt is obtained, and Using a second language model different from the first language model, for the prompt, a second text having less relevance than the relevance between the prompt and the first text, and To generate a data set in which the above prompt, the above first text, and the above second text are combined, causing the above electronic device, Electronic device.
2. In claim 1, the first language model is, With first complexity, and The above second language model is, Having a second complexity higher than the first complexity mentioned above, Electronic device.
3. In claim 1, the prompt is, It is the first prompt, and When the above instructions are executed individually or collectively by the at least one processor, Using the first language model or the second language model, obtain a second prompt that includes at least a portion of the first prompt and obtains a result different from the result requested by the first prompt, and By providing the second prompt to the second language model, a third text is obtained, and To generate another data set by combining the first prompt, the first text, and the third text, Causing the above electronic device, The relationship between the above second prompt and the above third text is, Less related than the relationship between the first prompt and the first text above, Electronic device.
4. In claim 3, When the above instructions are executed individually or collectively by the at least one processor, Using the first language model or the second language model, identify whether the first prompt is different from the second prompt, and Based on the determination that the above first prompt is different from the above second prompt: Obtain the third text for the second prompt above, and Generating the other data set by combining the first prompt, the first text, and the third text, and Based on the determination that the first prompt is not different from the second prompt, to refrain from obtaining the third text for the second prompt, causing the above electronic device, Electronic device.
5. In claim 3, When the above instructions are executed individually or collectively by the at least one processor, Using the first language model or the second language model, identify whether the first prompt is different from the second prompt, and Based on the determination that the above first prompt is different from the above second prompt: Obtain the third text for the second prompt above, and Generating the other data set by combining the first prompt, the first text, and the third text, and Based on the determination that the first prompt is not different from the second prompt, to refrain from generating the other data set, causing the above electronic device, Electronic device.
6. In claim 3, the second prompt is, The third text above includes at least one word that causes the second language model to contain an error, Electronic device.
7. In claim 1, the data set is, Used to perform training on a third language model to determine the text among the first text and the second text that is more relevant to the prompt in response to the prompt. Electronic device.
8. In electronic devices, Memory comprising one or more storage media and storing instructions; It includes at least one processor comprising processing circuitry, and When the above instructions are executed individually or collectively by the at least one processor, Using a language model, obtain the first text for the first prompt, and Generate a second prompt to provide the result requested by the first prompt, along with an error, and Using the above language model, obtain the second text for the above second prompt, and To generate a data set including the first prompt, the first text, and the second text, causing the above electronic device, Electronic device.
9. In claim 8, the second prompt is, at least one word that causes the language model to include false information, Electronic device.
10. In claim 8, the second prompt is, including at least one word that causes the language model to produce an incomplete result, Electronic device.
11. In claim 8, the second prompt is, The above second text includes at least one word that causes the language model to include another result different from the result requested by the first prompt, Electronic device.
12. In claim 8, the second prompt is, The second text includes at least one word that causes the language model to omit a part of the result requested by the first prompt, Electronic device.
13. In claim 8, the second prompt is, The above second text includes at least one word that causes the language model to include a different result for a different request that is different from the request by the first prompt, Electronic device.
14. In claim 8, When the above instructions are executed individually or collectively by the at least one processor, Identifying whether the above second text includes the above result and the above error requested by the above first prompt, and Based on the determination that the second text above includes the result and the error, the data set is generated, and To refrain from generating the data set based on the determination that the above second text does not include the above result and the above error, causing the above electronic device, Electronic device.
15. In claim 8, the language model is, It is a first language model, and When the above instructions are executed individually or collectively by the at least one processor, Using a second language model having a second complexity higher than the first complexity of the first language model, a third text for the second prompt is obtained, and To generate another data set including the first prompt, the first text, and the third text, causing the above electronic device, Electronic device.
16. In claim 8, the data set is, Used to perform training on a third language model to determine, in response to the first prompt, the text between the first text and the second text that is more relevant to the first prompt. Electronic device.
17. In a non-transient computer-readable storage medium storing one or more programs, said one or more programs, when executed by an electronic device, By providing a prompt to a first language model, a first text for the prompt is obtained, and Using a second language model different from the first language model, for the prompt, a second text having less relevance than the relevance between the prompt and the first text, and To generate a data set in which the above prompt, the above first text, and the above second text are combined, Including instructions that cause the above electronic device, Non-transient computer-readable storage media.
18. In claim 17, the first language model is, With first complexity, and The above second language model is, Having a second complexity higher than the first complexity mentioned above, Non-transient computer-readable storage media.
19. In claim 17, the prompt is, It is the first prompt, and When the above one or more programs are executed by the electronic device, Using the first language model or the second language model, obtain a second prompt that includes at least a portion of the first prompt and obtains a result different from the result requested by the first prompt, and By providing the second prompt to the second language model, a third text is obtained, and To generate another data set by combining the first prompt, the first text, and the third text, Includes instructions that cause the above electronic device, The relationship between the above second prompt and the above third text is, Less related than the relationship between the first prompt and the first text above, Non-transient computer-readable storage media.
20. In claim 19, When the above one or more programs are executed by the electronic device, Using the above second language model, identify whether the above first prompt is different from the above second prompt, and Based on the determination that the above first prompt is different from the above second prompt: Obtain the third text for the second prompt above, and Generating the other data set by combining the first prompt, the first text, and the third text, and Based on the determination that the first prompt is not different from the second prompt, to refrain from obtaining the third text for the second prompt, Including instructions that cause the above electronic device, Non-transient computer-readable storage media.

Description

Electronic device, method, and non-transient computer-readable storage medium for generating a data set for training a model The present disclosure relates to an electronic device, a method, and a non-transient computer-readable storage medium for generating a data set for training a model. An electronic device may include a large language model (LM). The electronic device may receive user input representing a prompt. By providing the prompt to the LLM, the electronic device may obtain a result for the prompt. The LLM may be trained with data to provide a result for the prompt. The LLM may be used to provide a result corresponding to the request by identifying the request within the prompt. The information described above may be provided as related art for the purpose of aiding understanding of the present disclosure. No claim or determination is made as to whether any of the foregoing can be applied as prior art related to the present disclosure. Figure 1 illustrates an example of an electronic device that obtains a response to a prompt. Figure 2 is a simplified block diagram of an exemplary electronic device. Figure 3 is a flowchart illustrating the operation of an electronic device that generates a data set using multiple models. FIGS. 4a and 4b illustrate an exemplary operation of an electronic device for acquiring text using multiple models. FIG. 5 is a flowchart illustrating the operation of an electronic device that identifies whether the first prompt and the second prompt are different. Figure 6 is a flowchart illustrating the operation of an electronic device that generates a data set using multiple prompts. FIG. 7 illustrates an exemplary operation of an electronic device that acquires text using multiple prompts. FIG. 8 is a flowchart illustrating the operation of an electronic device that identifies whether a second text contains an error. Figure 1 illustrates an example of an electronic device that obtains a response to a prompt. Referring to FIG. 1, an electronic device (100) may be used to obtain a response to a prompt. For example, the electronic device (100) may include a language model (e.g., the first language model (430) of FIG. 4a). For example, the language model may be referred to as a large language model (LLM) or a large language model. For example, the electronic device (100) may obtain a result (e.g., text or image) by providing a prompt to the language model. For example, the language model may include a model trained through machine learning techniques. For example, the language model may include a model trained through deep learning techniques. For example, the language model may include a model trained through artificial neural network techniques. For example, the prompt may be described as a command or question entered into a computer, program, or system. For example, the prompt may be in the form of natural language. For example, an electronic device (100) may receive user input indicating a prompt (120). For example, the electronic device (100) may obtain the prompt (120) based on the user input. For example, the prompt (120) may include text asking to be told three habits for a healthy life. For example, the electronic device (100) may obtain a first response (140) by providing the prompt (120) to a language model. For example, the language model may provide the first response (140) in response to the receipt of the prompt (120). For example, the electronic device (100) may obtain a second response (160) by providing a prompt (120) to another language model. For example, the other language model may provide a second response (160) in response to receiving the prompt (120). For example, the language model may be a different model from the other language model. For example, the dataset used to train the language model may be different from the dataset used to train the other language model. For example, the weights for each layer of the language model may be different from the weights for each layer of the other language model. For example, the parameters of the language model may be different from the parameters of the other language model. However, it is not limited thereto. For example, the language model may be the same as the other language model. For example, the other language model identical to the language model may provide a second response (160) that is different from the first response (140). An electronic device (100) may be used to determine which of the first response (140) and the second response (160) is more relevant to the prompt (120). For example, the electronic device (100) may use a language model to determine which of the first response (140) and the second response (160) is more relevant to the prompt (120). For example, the electronic device (100) may obtain information about which of the first response (140) and the second response (160) is more relevant to the prompt (120) by providing the prompt (120), the first response (140), and the second res