We're Afraid Language Models Aren't Modeling Ambiguity - Summary
Blog post from Portkey
The paper explores the critical role of managing ambiguity in natural language understanding and assesses the capability of language models to identify and differentiate potential meanings. It introduces AMBIENT, a linguist-annotated benchmark consisting of 1,645 examples showcasing various ambiguities, and uses it to create tests for evaluating pretrained language models, revealing that recognizing ambiguity is still a significant challenge even for advanced models like GPT-4. Furthermore, the study includes a case study demonstrating how a multilabel natural language inference (NLI) model can effectively detect misleading political claims in real-world scenarios, underscoring the importance of handling ambiguity for the success of language models.
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