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