Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour
This addresses the problem of reporting bias in language models for AI researchers, showing that scaling can mitigate certain biases, though it is incremental as it builds on prior studies of smaller models.
The study investigated whether scaling up language models (LLMs) like PaLM and GPT-3 exacerbates reporting bias in determining typical object colors, finding that LLMs significantly outperform smaller models and better align with human judgments, reducing bias.
Language models (LMs) trained on raw texts have no direct access to the physical world. Gordon and Van Durme (2013) point out that LMs can thus suffer from reporting bias: texts rarely report on common facts, instead focusing on the unusual aspects of a situation. If LMs are only trained on text corpora and naively memorise local co-occurrence statistics, they thus naturally would learn a biased view of the physical world. While prior studies have repeatedly verified that LMs of smaller scales (e.g., RoBERTa, GPT-2) amplify reporting bias, it remains unknown whether such trends continue when models are scaled up. We investigate reporting bias from the perspective of colour in larger language models (LLMs) such as PaLM and GPT-3. Specifically, we query LLMs for the typical colour of objects, which is one simple type of perceptually grounded physical common sense. Surprisingly, we find that LLMs significantly outperform smaller LMs in determining an object's typical colour and more closely track human judgments, instead of overfitting to surface patterns stored in texts. This suggests that very large models of language alone are able to overcome certain types of reporting bias that are characterized by local co-occurrences.