metadata

dataset_info:
  - config_name: radar
    features:
      - name: task_id
        dtype: string
      - name: query
        dtype: string
      - name: answer
        dtype: string
      - name: artifact_type
        dtype: string
      - name: artifact_scope
        dtype: string
      - name: query_cols
        sequence: string
      - name: artifact_reasoning_cols
        sequence: string
      - name: table
        struct:
          - name: headers
            large_list: string
          - name: rows
            large_list:
              large_list: string
      - name: num_rows
        dtype: int64
      - name: num_cols
        dtype: int64
      - name: recovered_tables_transform_spec
        sequence:
          - name: drop_rows
            large_list: int64
          - name: overwrite_cells
            large_list:
              - name: row
                dtype: int64
              - name: col
                dtype: string
              - name: new_value
                dtype: string
      - name: base_data_num_tokens
        dtype: int64
      - name: base_data_token_bucket
        dtype: int64
      - name: perturbation_note
        dtype: string
    splits:
      - name: test
        num_bytes: 54326034
        num_examples: 2980
    download_size: 5735883
    dataset_size: 54326034
  - config_name: radar-sizes
    features:
      - name: task_id
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      - name: query
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      - name: answer
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      - name: artifact_scope
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      - name: query_cols
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      - name: artifact_reasoning_cols
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          - name: rows
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              - name: col
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              - name: new_value
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      - name: base_data_num_tokens
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        dtype: int64
      - name: perturbation_note
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      - name: test
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        num_examples: 720
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    dataset_size: 12508093
  - config_name: radar-tasks
    features:
      - name: task_id
        dtype: string
      - name: query
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      - name: answer
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      - name: artifact_scope
        dtype: string
      - name: query_cols
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      - name: artifact_reasoning_cols
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      - name: table
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          - name: headers
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          - name: rows
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      - name: num_rows
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      - name: num_cols
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      - name: recovered_tables_transform_spec
        sequence:
          - name: drop_rows
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              - name: row
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              - name: col
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              - name: new_value
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      - name: base_data_num_tokens
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      - name: base_data_token_bucket
        dtype: int64
      - name: perturbation_note
        dtype: string
    splits:
      - name: test
        num_bytes: 6116305
        num_examples: 313
    download_size: 830488
    dataset_size: 6116305
configs:
  - config_name: radar
    data_files:
      - split: test
        path: radar/test-*
  - config_name: radar-sizes
    data_files:
      - split: test
        path: radar-sizes/test-*
  - config_name: radar-tasks
    data_files:
      - split: test
        path: radar-tasks/test-*
license: cc-by-4.0
task_categories:
  - table-question-answering
language:
  - en
pretty_name: RADAR
size_categories:
  - 1K<n<10K

RADAR: Benchmarking Language Models on Imperfect Tabular Data

Link: Paper | Code

The Robust And Data Aware Reasoning (RADAR) benchmark is designed to evaluate the ability of language models to demonstrate data-awareness—that is, to recognize, reason over, and appropriately handle complex data artifacts such as:

Missing data
Bad values
Outliers
Inconsistent formatting
Inconsistent multi-column logic

The full dataset includes 53 tasks grounded in real-world data tables and varies across data artifact types and table dimensions (by token count and number of columns). In total, RADAR provides 2,980 unique query-table task instances. We also include two subsets of the data: (1) radar-sizes (RADAR-S) to focus evaluation on table sizes and (2) radar-tasks (RADAR-T) to focus evaluation across all tasks.

📊 Dataset Statistics

Dataset Split	Tasks	Instances	Tokens (K)	Cols
RADAR	53	2,980	[2,4,8,16]	[5,10,20]
RADAR-T	53	313	8	10
RADAR-S	10	720	[2,4,8,16]	[5,10,20]

🔭 Dataset Structure

Each task instance comprises of the follwowing data:

task_id: a unique id for each source table and query
query: the query to ask over the data table
answer: ground truth answer to the query
artifact_type: the artifact type introduced to the data table for this task
artiact_scope: does reasoning over the data artifacts involve only a single column, naively or independetly over multiple columns, or jointly or connected over multiple columns
query_cols: the columns invovled in the query
artifact_reasoning_cols: the columns invovled in reasoning over the artifacts
table: the data table for this task (a dictionary with keys "headers" and "rows" to represent the table column names and rows)
num_rows: number of rows in the tbale
num_cols: number of columns in the table
recovered_tables_transform_spec: The right answer is caluclated over the recovered data table(s). We convert the data table in table to the recovered data table(s) using this specification indicating which rows to drop and which cells to overwrite.
base_data_num_tokens: The number of tokens in the data table (before introducing any data artifact perturbations). This may be slightly different after introducing perturbations.
base_data_token_bucket: The token bucket in which this task belongs to (one of 2000, 4000, 8000, and 16000)
perturbation_note: Any note about the data artifact perturbation that is introduced.

💻 Loading the Data

Using Hugging Face

from datasets import load_dataset
radar_all = load_dataset("kenqgu/radar", "radar")["test"]
radar_s = load_dataset("kenqgu/radar", "radar-sizes")["test"]
radar_t = load_dataset("kenqgu/radar", "radar-tasks")["test"]

Using included RADAR code to load into more usable pydantic objects (need to install radar first).

from radar.data import load_task_instances_hf

# load the full dataset
tasks, task_summary_df = load_task_instances_hf(split="full")
tasks_s, _ = load_task_instances_hf(split="sizes")
tasks_t, _ = load_task_instances_hf(split="tasks")

# view the table as a pandas dataframe
tasks[0].table_df.head()

📖 Citation

If you use RADAR in your research, please cite our paper:

@article{gu2025radar,
  title={RADAR: Benchmarking Language Models on Imperfect Tabular Data}, 
  author={Ken Gu and Zhihan Zhang and Kate Lin and Yuwei Zhang and Akshay Paruchuri and Hong Yu and Mehran Kazemi and Kumar Ayush and A. Ali Heydari and Maxwell A. Xu and Girish Narayanswamy and Yun Liu and Ming-Zher Poh and Yuzhe Yang and Mark Malhotra and Shwetak Patel and Hamid Palangi and Xuhai Xu and Daniel McDuff and Tim Althoff and Xin Liu},
  year={2025},
  eprint={2506.08249},
  archivePrefix={arXiv},
  primaryClass={cs.DB},
  url={https://arxiv.org/abs/2506.08249}, 
}