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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'multipref' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Column() changed from object to string in row 0
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 174, in _generate_tables
                  df = pandas_read_json(f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1025, in read
                  obj = self._get_object_parser(self.data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1051, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1187, in parse
                  self._parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1402, in _parse
                  self.obj = DataFrame(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/frame.py", line 778, in __init__
                  mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 503, in dict_to_mgr
                  return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 114, in arrays_to_mgr
                  index = _extract_index(arrays)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 680, in _extract_index
                  raise ValueError(
              ValueError: Mixing dicts with non-Series may lead to ambiguous ordering.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 499, in _iter_arrow
                  for key, pa_table in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 346, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 177, in _generate_tables
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 151, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0

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Feedback Forensics Annotations

This dataset contains the personality annotations from the experiments included in the Feedback Forensics paper. The annotations are provided over pairwise model outputs, typically consisting of a prompt and two responses. Each individual annotation considers as single personality trait (e.g. "confidence"). The annotation indicates when two responses differ with respect to that trait (e.g. one response is more confident), a trait does not apply to both responses or is equal in both responses. Annotations were collected using the Inverse Constitutional AI (ICAI) annotation pipeline with a fixed set of personality traits to be tested, using gemini-2.5-flash. The data is provided in the AnnotatedPairs json data format, described here.

Note that we do not include prompts and responses from the original datasets, instead providing metadata (e.g., conversation_id) to enable merging with the base data. The annotation data is sufficient for independent local analysis with the Feedback Forensics Gradio app, even without merging.

Format

The data is provided in the AnnotatedPairs json data format, described here. See a minimal illustrative example below:

{
  "metadata": {
    "version": "2.0",
    "description": "Annotated pairs dataset with annotations from ICAI",
    "created_at": "2025-04-02",
    "dataset_name": "dataset",
    "default_annotator": "d36860d4"
  },
  "annotators": {
    "d36860d4": {
      "name": "Human",
      "description": "Human annotator from original dataset",
      "type": "human"
    },
    "2f45a6d0": {
      "description": "Select the response that evokes a sense of mystery.",
      "type": "principle"
    },
    "435cef52": {
      "description": "Select the response that features a more adventurous setting.",
      "type": "principle"
    }
  },
  "comparisons": [
    {
      "id": "4b49a6e7",
      "prompt": "Write a short story about a pet.",
      "response_a": {
        "text": "In the heart of a bustling city, a sleek black cat named Shadow prowled the moonlit rooftops, her eyes gleaming with curiosity and mischief. She discovered a hidden garden atop an old apartment building, where she danced under the stars, chasing fireflies that glowed like tiny lanterns. As dawn painted the sky in hues of orange and pink, Shadow found her way back home, carrying the secret of the garden in her heart.",
        "model": "Model X"
      },
      "response_b": {
        "text": "Across the town, in a cozy neighborhood, a golden retriever named Buddy embarked on his daily adventure, tail wagging with uncontainable excitement. He found a lost toy under the bushes in the park, its colors faded and fabric worn, but to Buddy, it was a treasure untold. Returning home with his newfound prize, Buddy's joyful barks filled the air, reminding everyone in the house that happiness can be found in the simplest of things.",
        "model": "Model Y"
      },
      "annotations": {
        "d36860d4": {
          "pref": "a"
        },
        "2f45a6d0": {
          "pref": "a"
        },
        "435cef52": {
          "pref": "a"
        }
      },
      "metadata": {
        "index": "0"
      }
    }
  ]
}

The main annotations are included under dataset['comparisons'][i]["annotations"], where i is the index of the annotation. dataset['comparisons'][i]["metadata"] provides metadata to enable merging.

Annotated datsets

We provide annotations for (subsets) of the following datasets:

Dataset License Source
MultiPref ODC-By (MultiPref), subsets vary MultiPref on HuggingFace
PRISM CC-BY-4.0 (prompts), CC-BY-NC-4.0 (responses), plus model terms PRISM on HuggingFace
Chatbot Arena
└ Arena Explorer release CC-BY-4.0 (prompts), model terms of use (responses) Arena Explorer on HuggingFace
└ Llama-4-Maverick release CC-BY-4.0 (prompts), model terms of use (responses) Llama-4-Maverick on HuggingFace
FF-Model-Personality ODC-By and CC-BY-4.0 (prompts), model terms of use (responses) FF-Model-Personality on Huggingface

We further include the human and AI annotations (human_personality_annotations.json) used for validating our AI annotators.

License

This dataset is licensed under Open Data Commons License Attribution License (ODC-By) (https://opendatacommons.org/licenses/by/1-0/).

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