Data Record Ranking for LLM Message Generation
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Solution Overview
Problem
Large language models (LLMs) often generate text that is confusing or incorrect in message environments, particularly in computer support scenarios where multiple concurrent issues exist, leading to errors such as generating text for the wrong opportunity or 'hallucination' of information from multiple sources.
Innovation Solution
The proposed solution involves ranking data records associated with a message source based on the similarity of features extracted from an incoming message with features in each data record, presenting the ranked records to a user interface, and dynamically generating an outgoing message using a selected data record from the language model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLM is used to automatically generate messages without data record ranking, then message generation speed is improved, but accuracy and reliability of generated text deteriorates
Solution Approach 1:
The system performs preliminary ranking of data records based on similarity to the incoming message before the LLM generates the response. This pre-processing step ensures that the most relevant data records are identified and presented to the user beforehand, allowing the LLM to generate accurate text based on the correct context without sacrificing generation speed.
2Loss of information
If LLM generates text using multiple data records simultaneously, then information completeness is improved, but text accuracy deteriorates due to hallucination and blending errors
Solution Approach 1:
The system segments the data records by ranking them individually based on their similarity to the incoming message. Instead of feeding multiple unranked data records to the LLM simultaneously, the system presents ranked data records in order of relevance, allowing the user to select the most appropriate one. This segmentation prevents the LLM from hallucinating or blending information from multiple unrelated data records.
3Manufacturing precision
If user heavily scrutinizes LLM-generated text for errors, then text accuracy is improved, but productivity deteriorates due to reduced automation value
Solution Approach 1:
The system performs the critical accuracy-checking function automatically through data record ranking before the LLM generates the text. By pre-rank data records based on similarity metrics and presenting the most relevant ones to the user, the system eliminates the need for users to manually scrutinize generated text for contextual accuracy, thereby maintaining high automation efficiency while ensuring text accuracy.
Data Source
AI summary
Solutions for ranking data records, in order to automatically generate message (e.g., email), extract features from an incoming message and identify data records (e.g., opportunities) associated with the message source within a data source. Features within each data record (e.g., opportunity title, names, products, and times) are matched against the incoming message features to rank the data records (e.g., rank opportunities against an incoming message). The ranking is presented to a user in a user interface (UI), and a language model dynamically generates outgoing message. The user may endorse the selection of the top-ranked data record, or select another data record for the language model to dynamically generate outgoing message.