In-Context Self-Healing for Toxic Language Model Outputs
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Solution Overview
Problem
Language models trained on massive, weakly curated text corpora inherit biases and produce toxic outputs, necessitating a method to mitigate these biases and control toxicity in generated text.
Innovation Solution
Implementing self-healing mechanisms within language models using In-Context Learning (ICL) to detect and reformulate toxic sequences, leveraging generative pre-trained transformers (GPT-3) for toxicity detection and reformulation, and utilizing snowballing to iteratively enhance prompt robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If language models are trained on massive web-scraped corpora to improve generation capability, then productivity and fluency are improved, but toxicity and biases increase
Solution Approach 1:
An external classifier is introduced as an intermediary component that evaluates generated text for toxicity. The classifier acts as a mediator between the language model's generation capability and the final output, filtering out toxic content while preserving benign generations. This allows the system to maintain high productivity from the large corpus training while mitigating toxicity through the intermediary evaluation layer.
Solution Approach 2:
The system implements a feedback loop where the external classifier's toxicity evaluations are used to guide prompt refinement. When toxic text is detected, the system provides feedback by adjusting the prompt to avoid triggering toxic responses. This iterative feedback mechanism allows the language model to learn from toxicity patterns without requiring retraining on the entire corpus, thus maintaining productivity while reducing harmful outputs.
2Object-affected harmful factors
If prompts are refined iteratively to reduce toxicity, then toxicity is reduced, but computational time and complexity increase
Solution Approach 1:
The system applies partial action by only refining prompts when toxic text is detected by the external classifier. Instead of iteratively refining all prompts regardless of outcome, the system selectively applies refinement only to cases where toxicity occurs. This reduces the overall computational time and resource expenditure while still achieving toxicity reduction where needed.
Solution Approach 2:
The system performs preliminary action by using the external classifier to evaluate generated text before final output. This preliminary evaluation allows the system to identify toxic responses early in the generation process and trigger prompt refinement only when necessary, rather than requiring extensive iterative refinement of all prompts beforehand. This approach saves computational time by avoiding unnecessary refinement cycles for benign generations.
Data Source
AI summary
A method for a language model applies in-context learning to detect problematic text and reformulate the problematic text to correct problematic text by (a) receiving, in the language model, a user generated text example; (b) determining if the user generated text example is a problematic text having a determined classification; (c) reformulating the user generated text example if the text example is a problematic text having the determined classification; (d) outputting the user generated text example if the text example is determined to be not a problematic text having the determined classification; and (e) outputting the reformulated text example if the text example is determined to be a problematic text having the determined classification.