LLM Starter Text Generation With Bias Threshold Filtering
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Solution Overview
Problem
Large-scale machine-learned language models often incorporate biases from training data, which are reflected in generated text, compromising the effectiveness and fairness of electronic documents.
Innovation Solution
A user interface is developed to allow users to input starter text, with features to detect and mitigate biases in candidate texts generated by machine-learned language models, ensuring the text meets predetermined quality thresholds before insertion into documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine-learned language models are used to generate text, then text generation productivity is improved, but bias and harmful factors are introduced into the generated text
Solution Approach 1:
The system implements a feedback mechanism where generated text is evaluated against a set of features (including bias detection) and the evaluation results are fed back to filter and select only acceptable text for insertion into the electronic document. This continuous feedback loop ensures that harmful factors are identified and eliminated while maintaining high productivity.
Solution Approach 2:
An intermediary evaluation system is introduced between the language model and the final document output. This intermediary component analyzes generated text against predefined features and acts as a gatekeeper, allowing only text that passes the bias and harmful factor detection to proceed to the document insertion stage.
2Speed
If machine-learned language models are used to generate text, then text generation speed is improved, but text quality and fairness are compromised
Solution Approach 1:
The system continuously monitors and evaluates generated text against quality and fairness criteria, using feedback to ensure that only text meeting predetermined standards is inserted into documents. This maintains both speed and reliability by filtering out low-quality or biased text without significantly impacting generation velocity.
Solution Approach 2:
The system replaces manual review mechanisms with automated machine-based evaluation of generated text against predefined features. This substitution maintains the speed advantage of AI generation while ensuring reliability through systematic, objective assessment of text quality and fairness metrics.
3Object-generated harmful factors
If bias detection and mitigation features are added to the system, then text fairness is improved, but device complexity increases
Solution Approach 1:
The bias detection and mitigation system is segmented into distinct modular components, each responsible for specific evaluation tasks. This segmentation allows the complex functionality to be managed through smaller, independent units that can be developed, tested, and maintained separately, reducing overall system complexity while maintaining comprehensive bias mitigation capabilities.
Solution Approach 2:
The system uses parameter-based evaluation where bias detection is framed as analyzing text against predefined feature parameters and thresholds. By transforming complex qualitative bias assessment into quantitative parameter comparison, the system simplifies the detection mechanism and makes it more manageable despite the comprehensive scope of bias mitigation requirements.
Data Source
AI summary
A server displays a user interface configured to allow a user to enter and edit an electronic document. Responsive to receiving an indication from a user to generate starter text, the server presents one or more topics and one or more keywords related to the topic on the interface for selection. The server generates a prompt to a machine-learned language model. The prompt may specify at least the selected topic, the selected keywords, and a request to generate a set of candidate texts incorporating the selected topic and the selected keywords. For each candidate starter text, the server detects issues for mitigation in the candidate text to evaluate whether a degree of the detected issue in the candidate text is less than a predetermined threshold.