## Screenshot: Example Task for Textual Information Extraction ### Overview The image is a screenshot displaying a template for extracting calculations and reasoning from a given question-answer pair. It contains structured sections with instructions and placeholders for input. ### Components/Axes - **Question:** Placeholder for a question ("ask question?") - **Answer:** Placeholder for an answer ("Answer with reasoning from LLM") - **Task:** Detailed instructions specifying the extraction of calculations performed to arrive at the answer - **Format Specification:** - Programmatic representation of operations (e.g., `OPERATION: "[arithmetic/logic]"`) - Argument types (e.g., `ARG1: "[float/int]"`) - Answer representation (e.g., `ANSWER: "[numerical/boolean]"`) ### Detailed Analysis - The structure is hierarchical: - **Program** includes numbered operations (`#0`, `#1`, ...) with operations and arguments - Each operation has a defined `OPERATION` type (arithmetic/logic) and arguments (`ARG1`, `ARG2`) with data types - The answer is represented as a numerical or boolean value ### Key Observations - Placeholder text indicates where user-provided content would be inserted - Strict formatting requirements are specified for operations and arguments - The example uses a nested JSON-like structure to represent operations and data types ### Interpretation This template demonstrates a method for breaking down reasoning steps into programmatic components, which could be used for: - Automated reasoning extraction from natural language explanations - Structured data generation for training AI models - Formal verification of AI reasoning processes The structured format enables unambiguous representation of calculations, potentially improving reproducibility and interpretability of AI-generated reasoning.