Language Model Reference Information Splicing for Toxic Prompt Handling
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Solution Overview
Problem
Current language models struggle to accurately identify toxic prompt information, leading to either excessively low generation rates for non-toxic responses or high miss rates for toxic responses, thus failing to achieve a balance between these two metrics.
Innovation Solution
A method that involves splicing first reference information for non-toxic instructions and second reference information for toxic instructions to generate third reference information, which is used to improve the language model's response generation by considering both types of instructions, thereby reducing miss rates and enhancing generation rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the language model focuses on identifying toxic prompt information, then the miss rate for toxic responses decreases, but the generation rate for non-toxic responses becomes excessively low
Solution Approach 1:
The reference information is segmented into two distinct types: first reference information for non-toxic instructions and second reference information for toxic instructions. This segmentation allows the language model to process different types of prompts with specialized reference information, improving both the detection accuracy for toxic prompts and the generation rate for non-toxic prompts by providing targeted guidance for each category.
2Productivity
If the language model prioritizes non-toxic response generation, then the generation rate increases, but the miss rate for toxic responses becomes high
Solution Approach 1:
Reference information serves as an intermediary that mediates between the prompt and the language model's response generation. By introducing this intermediate layer that contains guidance for both toxic and non-toxic scenarios, the system can maintain high generation rates while improving toxic response detection, as the reference information provides the model with explicit handling instructions for different prompt types.
3Device complexity
If the language model uses a single reference information set, then the system complexity is low, but it cannot achieve balanced performance for both toxic and non-toxic instructions
Solution Approach 1:
The system dynamically selects and combines different reference information based on the characteristics of each prompt. Rather than using a static single reference set, the system adaptively determines which reference information (first for non-toxic, second for toxic) to apply to each prompt, enabling balanced performance across different instruction types while maintaining manageable system complexity through dynamic adaptation.
Data Source
AI summary
The present disclosure relates to the field of computer technologies, and discloses a reply method and system, a device, and a storage medium. The reply method includes: receiving a target instruction to be replied to by a language model; obtaining first reference information used by the language model when replying to a non-toxic instruction, and obtaining second reference information used by the language model when replying to a toxic instruction; splicing the first reference information and the second reference information to obtain third reference information needed for replying to the target instruction; and inputting the target instruction and the third reference information into the language model to cause the language model to generate reply content for the target instruction based on the third reference information.