Learning the Error Patterns of Language Models
Researchers propose Palla, an algorithm that learns symbolic constraint functions called prefix filters to capture and correct systematic error patterns in large language models. By analyzing domain-specific failures (e.g., using Python syntax in TypeScript code), Palla enables constrained sampling to significantly improve compilation rates and output validity without retraining models.