Attention-Refined Prompt Discovery for Accurate LLM Task Alignment
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Solution Overview
Problem
Current neural network models face issues with misinterpretation and inaccurate outputs due to ill-defined prompts and disjointedness in prompt engineering, particularly in tasks where labeled data is scarce.
Innovation Solution
The method employs attention refinement through sequence sensing, segmentation, and bin packing using Minkowski distance metrics to correlate prompt and text embeddings, leveraging techniques like zero-shot, one-shot, and few-shot learning, and a 5W framework for computing attention weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If prompts are templated or pre-appended to a task, then the task can be executed with available data, but the prompt can be ill-defined for an LLM in both understandability and disjointedness leading to misinterpretation or inaccurate output
Solution Approach 1:
The patent segments the input sequence into multiple components including prompt tokens, text tokens, and attention embeddings. This segmentation allows the system to separately process and analyze different elements of the prompt structure, improving understandability by identifying which segments contribute most to the task while maintaining productivity through automated processing.
Solution Approach 2:
The patent changes parameters by computing attention weights and attention embeddings that dynamically adjust the importance of different prompt segments. This parameter transformation converts static templated prompts into dynamically weighted representations, resolving the contradiction between structured task execution and prompt clarity.
2Ease of operation
If prompts are pre-appended to describe the desired task, then the model receives task guidance, but the prompt can be disjointed leading to inaccurate output from diminished weights
Solution Approach 1:
The patent implements feedback by computing attention weights that indicate how well each prompt segment aligns with the task. This feedback mechanism allows the system to identify and correct disjointed or ineffective prompt segments, improving output reliability while maintaining ease of operation through automated prompt optimization.
Solution Approach 2:
The patent introduces dynamics by transforming static prompts into dynamic representations through attention mechanisms. The attention weights and embeddings dynamically adjust based on the task requirements, allowing the prompt structure to adapt and resolve disjointedness while preserving task guidance capabilities.
3Measurement precision
If bin packing is used to match prompt embedding to bins defined by attention embedding, then the relationship between prompt and text is computed more accurately, but the computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing attention embeddings and organizing them into bins before the main matching process. This preprocessing step structures the data in advance, allowing the subsequent bin packing and Minkowski distance matching to proceed more efficiently, thus reducing overall computational complexity while maintaining high measurement precision.
Data Source
AI summary
An embodiment senses a sequence, responsive to the sensed sequence, segments the sensed sequence into a prompt and text. The embodiment generates an attention embedding representative of the text and computes an attention weight based on the attention embedding. The embodiment generates a prompt embedding representative of the prompt. The embodiment computes a relationship between the prompt embedding and the attention weight comprising correlating the attention embedding and the attention weight based on a beam search; bin packing the prompt embedding where a bin is defined by the attention embedding; matching the prompt embedding to the bin based on a Minkowski distance metric; where the bin packing causes the computing of the relationship between the prompt and the text represented by the attention weight.