Language Model Instruction Verification and Correction System
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Solution Overview
Problem
Current language models perform poorly in following instructions, especially when multiple instructions are present in a prompt, leading to incomplete or inaccurate responses.
Innovation Solution
A system comprising a response generator, an instruction identifier, an instruction checker, and a fix prompt generator is implemented to improve the instruction-following ability of language models. This system generates a list of instructions from a prompt, verifies if the response follows each instruction, and if not, generates a fix prompt to ensure the model follows all instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If language models are fine-tuned on instruction-following examples, then the model's ability to follow instructions improves, but the computational cost and complexity of fine-tuning increases
Solution Approach 1:
The patent segments the instruction-following process into distinct components: instruction identification, instruction verification, and selective re-generation. This segmentation allows the model to focus computational resources only on instructions that were not followed, rather than re-processing the entire response, thereby reducing fine-tuning complexity while maintaining reliability
Solution Approach 2:
The patent implements preliminary action by having the language model generate a complete response first, then systematically verifying each instruction against the generated response. This approach allows the model to identify specific instructions that need correction before committing computational resources to re-generation, optimizing the fine-tuning process
2Productivity
If language models are asked to follow multiple instructions in a prompt, then the comprehensiveness of the response improves, but the model's ability to follow all instructions accurately deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the generated response is systematically checked against each identified instruction. Instructions that are not followed are fed back into the model for correction, creating a closed-loop system that ensures all instructions are ultimately satisfied, thereby maintaining both comprehensiveness and accuracy
Solution Approach 2:
The patent segments multiple instructions into individually verifiable components, allowing the system to track which specific instructions were followed and which were missed. This segmentation enables precise targeting of corrections without compromising the overall response structure, maintaining both productivity and measurement precision
3Measurement precision
If the system verifies each instruction in the prompt, then the accuracy of instruction following improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by verifying only the necessary instructions against the generated response - specifically focusing on instructions that were not followed. Rather than uniformly processing all instructions with equal depth, the system performs targeted verification on missed instructions, reducing processing time while maintaining verification accuracy
Data Source
AI summary
Techniques for ensuring that language models follow instructions indicated in prompts are provided. In one technique, a first language model generates a response based on a prompt. A set of instructions in the prompt is identified. For each instruction in the set, a second language model determines whether the response indicates that the first language model followed the instruction. In another technique, for each prompt of a plurality of prompts: (1) a first language model generates a response based on the prompt; (2) multiple instructions are identified based on the prompt; (3) a second language model generates, based on the plurality of instructions, an output that indicates that the first language model followed each instruction; and (4) the prompt, the response, and the multiple instructions are stored in a training instance. The first language model is finetuned based on the training instances.