| --- |
| license: apache-2.0 |
| license_name: apache-license-version-2.0 |
| license_link: https://www.apache.org/licenses/LICENSE-2.0.txt |
| language: |
| - en |
| --- |
| |
| # Phantom: A Benchmark for Hallucination Detection in Financial Long-Context QA |
|
|
| Authors: Lanlan Ji, Dominic Seyler, Gunkirat Kaur, Manjunath Hegde, Koustuv Dasgupta, Bing Xiang |
|
|
| This is the repository containing the dataset for the submission mentioned above. |
|
|
| This dataset is designed for hallucination detection in language models. It includes multiple variants of the *Phantom* dataset with different token lengths (seed, 2k, 5K, 10K, 20K, 30K) for long context experiments , segments (beginning, middle, end), and filing types (e.g., 10K, DEF14A). |
|
|
| ## Dataset Structure |
|
|
| The dataset is organized into multiple **files**, each representing a specific dataset version. Examples include: |
|
|
| - `10K_seed` |
| - `497K_seed` |
| - `def14A_5000tokens_middle` |
| - `10K_30000tokens_end` |
| - ... and more |
|
|
| Each dataset is a single CSV file and is treated as a separate dataset with only a `train` split. |
|
|
| ## How to Use |
|
|
| To load a specific dataset configuration using the 🤗 `datasets` library: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Example: Load the 10K 10000-tokens (middle) dataset |
| df = load_dataset("seyled/Phantom_Hallucination_Detection", data_files="PhantomDataset/Phantom_10K_10000tokens_middle.csv") |
| |
| ``` |
| # Cite |
|
|
| ``` |
| @inproceedings{ |
| ji2025phantom, |
| title={{PHANTOM}: A Benchmark for Hallucination Detection in Financial Long-Context {QA}}, |
| author={Lanlan Ji and Dominic Seyler and Gunkirat Kaur and Manjunath Hegde and Koustuv Dasgupta and Bing Xiang}, |
| booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, |
| year={2025}, |
| } |
| ``` |
|
|
| # License |
|
|
| Licensed under the Apache License, Version 2.0 (the "License"); |
| you may not use this file except in compliance with the License. |
| You may obtain a copy of the License at |
|
|
| http://www.apache.org/licenses/LICENSE-2.0 |
| |
| Unless required by applicable law or agreed to in writing, software |
| distributed under the License is distributed on an "AS IS" BASIS, |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| See the License for the specific language governing permissions and |
| limitations under the License. |
