\n ## Textual Document: Causal Analysis Task Description ### Overview The image presents a textual description of a task for a "careful causality analyst acting as a reranker for retrieval." It outlines the goal, inputs, output format, and constraints of the task. The document appears to be a set of instructions for a machine learning model or a human annotator. ### Components/Axes The document is structured into sections denoted by "---[Section Title]---". The sections are: * **Role:** Describes the role of the analyst. * **Goal:** Defines the objective of the task. * **Inputs:** Specifies the input data. * **Output Format (JSON):** Details the expected output structure. * **Constraints:** Lists limitations on the output. ### Detailed Analysis or Content Details Here's a transcription of the text, section by section: **Role:** "You are a careful causality analyst acting as a reranker for retrieval." **Goal:** "Given a query and a list of context items (short ID + content), select the most important items consisting the causal graph and output them in ‘precise’. Also output the least important items as the spurious information in ‘ct_precise’. You MUST: - Use only the provided items. - Rank ‘precise’ from most important to least important. - Rank ‘ct_precise’ from least important to more important. - Output JSON only. Do not add markdown. - Use the short IDs exactly as shown. - Do NOT include any IDs in ‘p_answer’." **Inputs:** "Query: [query] Context Items (short ID | content): [context, table]" **Output Format (JSON):** ```json { 'precise': ['C1', 'N2', 'E3'], 'ct_precise': ['T7', 'N9'], 'p_answer': 'concise draft answer' } ``` **Constraints:** "- ‘precise’ length: at most (max_precise_items) items. - ‘ct_precise’ length: at most (max_ct_precise_items) items. - ‘p_answer’ length: at most (max_answer_words) words." ### Key Observations The document emphasizes the importance of adhering to the specified JSON output format and constraints. The task involves ranking context items based on their relevance to a given query, distinguishing between important causal factors ('precise') and spurious information ('ct_precise'). The use of short IDs is crucial, and they should not appear in the 'p_answer' field. The document uses placeholders like "[query]", "[context, table]", "(max_precise_items)", etc., indicating that these values will be provided as input. ### Interpretation This document describes a task designed to evaluate a system's ability to identify causal relationships within a set of contextual information. The "reranker" role suggests that the system is intended to refine an initial ranking of context items, potentially generated by a retrieval system. The separation of 'precise' and 'ct_precise' indicates a focus on filtering out irrelevant or misleading information. The constraints on output length suggest a need for concise and focused responses. The overall goal is to build a system that can accurately identify the key causal factors relevant to a given query, while discarding spurious information. The JSON output format is likely used for automated evaluation and integration with other components of a larger system.