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Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Do ...
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Abstract:
Artificial writing is permeating our lives due to recent advances in large-scale, transformer-based language models (LMs) such as BERT, its variants, GPT-2/3, and others. Using them as pre-trained models and fine-tuning them for specific tasks, researchers have extended state of the art for many NLP tasks and shown that they capture not only linguistic knowledge but also retain general knowledge implicitly present in the data. Unfortunately, LMs trained on unfiltered text corpora suffer from degenerated and biased behaviour. While this is well established, we show that recent LMs also contain human-like biases of what is right and wrong to do, some form of ethical and moral norms of the society -- they bring a "moral direction" to surface. That is, we show that these norms can be captured geometrically by a direction, which can be computed, e.g., by a PCA, in the embedding space, reflecting well the agreement of phrases to social norms implicitly expressed in the training texts and providing a path for ...
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Keyword:
Computation and Language cs.CL; Computers and Society cs.CY; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2103.11790
https://arxiv.org/abs/2103.11790
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