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+# MathDisAm: Towards Math Query Disambiguation
+
+Welcome to the official repository for the paper **MathDisAm: Towards Math Query Disambiguation**. This repository contains the dataset, query generation pipelines, and baseline classifier models designed to distinguish between ambiguous and unambiguous mathematical queries.
+
+## Dataset
+
+The `data` directory contains the core datasets used for training and evaluating the disambiguation models:
+* **`MathDisAM_train.tsv`**: The training file containing MathSE IDs, mathematical questions, and their corresponding tags ("Ambiguous" or "Unambiguous").
+* **`MathDisAM_test.tsv`**: The untouched, held-out test file used for final model evaluation.
+
+---
+
+## Query Generation 
+The query generation process leverages a two-agent Large Language Model pipeline (using Gemma) to process natural mathematical questions:
+
+* **Ambiguous Query Generation**: 
+  * Uses an "Injector" agent to rewrite a math question to introduce deliberate ambiguity. 
+  * Uses a "Verifier" agent to independently judge if the rewrite is genuinely ambiguous.
+  * The script processes questions until a target of 1,000 verified ambiguous questions is produced.
+* **Unambiguous Query Generation**: 
+  * Uses a "Clarifier" agent to rewrite queries to remove ambiguity. 
+  * Uses a "Verifier" agent to evaluate if the query is fully well-posed and unambiguous. 
+  * This pipeline also targets 1,000 verified unambiguous questions.
+
+---