Unnamed: 0 int64 0 999 | query stringlengths 31 280 | context stringlengths 1.27k 249k | answer stringlengths 1 4.61k | ground_truth_label stringclasses 2
values |
|---|---|---|---|---|
0 | What are some potential macroeconomic risks that could have an adverse impact on the company's business and financial results? | Water is an essential component of our drilling and hydraulic fracturing processes. If we are unable to obtain water to use in our operations from local sources, we may be unable to economically produce oil, natural gas liquids and natural gas, which could have an adverse effect on our business, financial condition and... | Macroeconomic risks that could have an adverse impact on the company's business and financial results include economic downturns, inflation, increases in interest rates, natural disasters, public health crises such as pandemics, political crises, geopolitical events, global or U.S. recession or other economic crisis, c... | not hallucination |
1 | What are some potential macroeconomic risks that could have an adverse impact on the company's business and financial results? | Water is an essential component of our drilling and hydraulic fracturing processes. If we are unable to obtain water to use in our operations from local sources, we may be unable to economically produce oil, natural gas liquids and natural gas, which could have an adverse effect on our business, financial condition and... | Macroeconomic risks that could have an adverse impact on the company's business and financial results include economic downturns, inflation, increases in interest rates, natural disasters, public health crises such as pandemics, geopolitical events, global or U.S. recession or other economic crisis, credit and capital ... | hallucination |
2 | What percentage of the company's net revenue was generated outside the United States in 2023? | Some of our competitors may possess greater resources, greater name recognition and longer operating histories than we do in particular markets or practice areas, or be willing to reduce their fees or agree to alternative pricing practices in order to attract clients and increase market share. Our competitors may be fu... | 41% | not hallucination |
3 | What percentage of the company's net revenue was generated outside the United States in 2023? | "Some of our competitors may possess greater resources, greater name recognition and longer operatin(...TRUNCATED) | "In 2023, the company generated approximately 41% of its net revenue outside the United States, prim(...TRUNCATED) | hallucination |
4 | "What is the company's policy regarding the use of derivative instruments or futures contracts for n(...TRUNCATED) | "Natural gas and NGLs are stored in large volumes in underground facilities and in smaller volumes i(...TRUNCATED) | "The company's policy is not to acquire and hold natural gas futures contracts or derivative product(...TRUNCATED) | not hallucination |
5 | "What is the company's policy regarding the use of derivative instruments or futures contracts for n(...TRUNCATED) | "Natural gas and NGLs are stored in large volumes in underground facilities and in smaller volumes i(...TRUNCATED) | "The company's policy is to utilize over-the-counter derivative instruments or enter into future del(...TRUNCATED) | hallucination |
6 | "Did the company's former auditor, MaloneBailey, express any adverse opinions or doubts about the co(...TRUNCATED) | "On June 16, 2023, MaloneBailey, LLP (“MaloneBailey”) informed the Company it would not stand fo(...TRUNCATED) | "No, MaloneBailey did not express any adverse opinions, but they did include an explanatory paragrap(...TRUNCATED) | not hallucination |
7 | "Did the company's former auditor, MaloneBailey, express any adverse opinions or doubts about the co(...TRUNCATED) | "On June 16, 2023, MaloneBailey, LLP (“MaloneBailey”) informed the Company it would not stand fo(...TRUNCATED) | "No, MaloneBailey did not express any adverse opinions, but they did include an explanatory paragrap(...TRUNCATED) | hallucination |
8 | "What is the prerequisite for a medical device to be marketed or sold in the EU, according to the EU(...TRUNCATED) | "Regulation of Medical Devices in the European UnionThe EU has adopted specific directives and regul(...TRUNCATED) | "Compliance with the general safety and performance requirements of the EU Medical Devices Regulatio(...TRUNCATED) | not hallucination |
9 | "What is the prerequisite for a medical device to be marketed or sold in the EU, according to the EU(...TRUNCATED) | "Regulation of Medical Devices in the European UnionThe EU has adopted specific directives and regul(...TRUNCATED) | "To be marketed or sold in the EU, a medical device must comply with the general safety and performa(...TRUNCATED) | hallucination |
End of preview. Expand in Data Studio
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_seed497K_seeddef14A_5000tokens_middle10K_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:
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.
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