Researchers pinpoint why larger language models pick up skills that small ones miss

Small language models fail at rare tasks because frequent ones constantly overwrite what they've learned. A new study with models ranging from 4 million to 4 billion parameters shows this mechanism in detail and offers a practical fix: instead of scaling up models, it may be enough to increase how often the target task appears in the training data. The article Researchers pinpoint why larger language models pick up skills that small ones miss appeared first on The Decoder .