Context-Aware Inclusive Language Detection With Feedback Tuning
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Solution Overview
Problem
Existing language models fail to effectively identify and adjust for exclusive language in organizational communication, which can alienate individuals and groups, leading to discrimination and reduced diversity in the workplace, despite good intentions, due to the lack of context-awareness and continuous tuning.
Innovation Solution
Implementing a large language model (LLM) with a context-aware inclusivity check plugin that collects user feedback to continuously adjust and refine the model, using prompt engineering and fine-tuning techniques to suggest inclusive language replacements, tailored to an organization's values and evolving language usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large language model is used to identify exclusive language in text, then the ability to detect and suggest inclusive language improvements is enhanced, but the model requires continuous tuning and user feedback to maintain accuracy and context-awareness
Solution Approach 1:
The system implements a feedback mechanism where user responses to inclusion recommendations are collected and used to continuously fine-tune the large language model. This closed-loop feedback system allows the model to learn from actual user interactions and improve its detection accuracy over time without requiring complete retraining, thus resolving the contradiction between maintaining high precision and managing tuning complexity.
Solution Approach 2:
The model is pre-trained on diverse datasets including inclusive language guidelines and organizational values before deployment. This preliminary action establishes a strong baseline capability for detecting exclusive language, reducing the amount of continuous tuning needed later and lowering the ongoing complexity while maintaining high detection accuracy.
2Adaptability or versatility
If the large language model is continuously tuned based on user feedback, then the context-awareness and inclusivity criterion satisfaction improve, but the time and computational resources required increase
Solution Approach 1:
The system implements dynamic tuning where the model adapts its learning intensity and frequency based on the quality and quantity of incoming feedback. Rather than continuous full retraining, the system uses incremental learning approaches that adjust model parameters efficiently, maintaining high context-awareness while minimizing time investment. The tuning process itself is optimized to be computationally efficient.
3Manufacturing precision
If the system provides detailed recommendations for altering text to satisfy inclusive-language criteria, then the quality of inclusivity improvement increases, but the complexity of the system architecture increases
Solution Approach 1:
The system architecture is segmented into distinct functional modules: text analysis module, recommendation generation module, feedback collection module, and model tuning module. Each module performs a specific function with well-defined interfaces. This segmentation allows the system to provide detailed, high-quality recommendations while managing architectural complexity through modular design, where each component can be developed and maintained independently.
Data Source
AI summary
A system can analyze first text that is received based on first user input data, and context of the first text, the analyzing using a large language model to identify a first recommendation to alter the first text to satisfy an inclusive-language criterion. The system can receive user feedback data based on the first recommendation. The system can tune the large language model based on the user feedback data, to produce an updated large language model. The system can analyze second text received based on second user input data with the updated large language model to identify a second recommendation to alter the second text to satisfy the inclusive-language criterion.