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configs:
- config_name: default
data_files:
- split: train
path: "**/*train*.tsv"
- split: test
path: "**/*test*.tsv"
size_categories:
- 1K<n<10K
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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.
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## 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.
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