Phys.org  September 14, 2023
Neural network language models appear to be increasingly aligned with how humans process and generate language, but identifying their weaknesses through adversarial examples is challenging due to the discrete nature of language and the complexity of human language perception. An international team of researchers (USA – Columbia University, Israel) turned the models against each other by generating controversial sentence pairs where two language models disagreed about which sentence is more likely to occur. Considering nine language models (including n-gram, recurrent neural networks, and transformers), they created hundreds of controversial sentence pairs through synthetic optimization or by selecting sentences from a corpus. Controversial sentence pairs proved highly effective at revealing model failures and identifying models that aligned most closely with human judgements of which sentence was more likely. The most human-consistent model tested was GPT-2, although experiments also revealed substantial shortcomings in its alignment with human perception… read more. TECHNICAL ARTICLE
Model comparison using synthetic sentences. Credit: Nature Machine Intelligence (2023)