When Small Models Learn From Their Mistakes: Arithmetic Reasoning Without Fine-Tuning
Numbers are where language models usually stop sounding impressive. Ask a model to summarize a financial report and it may produce a fluent paragraph with just enough confidence to make everyone in the meeting relax. Ask it to calculate a percentage change from a table, preserve the correct scale, and return a verifiable number, and the poetry ends. Suddenly the model must select the right values, understand the wording, apply the right operation, avoid sign mistakes, avoid scale mistakes, and not hallucinate a formula because the word “change” appeared nearby. ...