Enhancing Language Generation with Logical Constraints

In this article, we explore the use of neuro-symbolic AI in addressing the issue of toxic language generation. Toxic language refers to words or phrases that are offensive or hurtful, and can have serious consequences for individuals and society as a whole. Neuro-symbolic AI combines the strengths of both neural networks and symbolic approaches to learning and reasoning, with the goal of developing more explainable and trustworthy models.
We present results from our experiments using a state-of-the-art language model, GPT-2, and show how incorporating a pseudo-semantic loss function along with a neuro-symbolic approach (SGEAT) significantly improves the average toxicity of generated sentences while reducing perplexity. This approach has the potential to address some of the limitations of purely statistical or symbolic approaches, enabling more accurate and reliable language generation while minimizing offensive content.
To explain this complex concept in simpler terms, imagine a computer program that can generate text like a human writer. Just as a human writer might use words that are offensive or hurtful, a language model like GPT-2 can also produce toxic sentences. By adding a new component to the model, called SGEAT, we can make it less likely to generate these types of sentences. This is like giving the computer program a "filter" to remove offensive words before it generates text.
The Perspective API toxicity scores are used to evaluate the effectiveness of our approach. These scores range from 0 to 1, with higher scores indicating more toxic content. Our experiments show that SGEAT significantly reduces the average toxicity of generated sentences, while also improving perplexity (a measure of how well the model fits the data). This suggests that our approach is effective in addressing toxic language generation without sacrificing accuracy.
In summary, neuro-symbolic AI has shown promise in addressing the issue of toxic language generation. By combining the strengths of both neural networks and symbolic approaches, we can develop more explainable and trustworthy language models that are less likely to produce offensive content. This has important implications for a wide range of applications, from chatbots and virtual assistants to content creation and social media moderation.