Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation
Paper • 2605.25831 • Published
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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
id: string
generation_config: struct<model_name: string, mode: string, input_path: string, output_dir: string, output_fname: null, (... 556 chars omitted)
child 0, model_name: string
child 1, mode: string
child 2, input_path: string
child 3, output_dir: string
child 4, output_fname: null
child 5, max_examples: null
child 6, filter_conflicts: bool
child 7, data_seed: int64
child 8, direct_prompt: string
child 9, reasoner_prompt: string
child 10, final_prompt: null
child 11, no_greedy: null
child 12, sampling: string
child 13, belief_sampling: string
child 14, temperature: null
child 15, top_p: null
child 16, top_k: null
child 17, min_p: null
child 18, n_samples: int64
child 19, max_new_tokens: int64
child 20, use_8bit: bool
child 21, batch_size: null
child 22, max_input_tokens: int64
child 23, api_processing: string
child 24, assistant_model: string
child 25, user_model: null
child 26, generate_fn: string
child 27, resolved_generation_params: struct<n_samples: int64, max_new_tokens: int64, api_processing: string, temperature: null>
child 0, n_samples: int64
child 1, max_new_tokens: int64
child 2, api_processing: string
child 3, temperature: null
generations: struct<strategy: string, reasoning: string, summary: null, response: string, raw_response: string>
child 0, strategy: string
child 1, reasoning: string
child 2, summary: null
child 3, response: string
child 4, raw_response: string
prompt: list<item: struct<role: string, content: string>>
child 0, item: struct<role: string, content: string>
child 0, role: string
child 1, content: string
processed_prompt: list<item: struct<role: string, content: string>>
child 0, item: struct<role: string, content: string>
child 0, role: string
child 1, content: string
context: struct<question: string, belief_state: list<item: string>, references: list<item: list<item: string> (... 110 chars omitted)
child 0, question: string
child 1, belief_state: list<item: string>
child 0, item: string
child 2, references: list<item: list<item: string>>
child 0, item: list<item: string>
child 0, item: string
child 3, disambiguations: list<item: struct<question: string, answer: list<item: string>, annotation_index: int64>>
child 0, item: struct<question: string, answer: list<item: string>, annotation_index: int64>
child 0, question: string
child 1, answer: list<item: string>
child 0, item: string
child 2, annotation_index: int64
to
{'id': Value('string'), 'generation_config': {'model_name': Value('string'), 'mode': Value('string'), 'input_path': Value('string'), 'output_dir': Value('string'), 'output_fname': Value('string'), 'max_examples': Value('null'), 'filter_conflicts': Value('bool'), 'data_seed': Value('int64'), 'direct_prompt': Value('string'), 'reasoner_prompt': Value('string'), 'final_prompt': Value('null'), 'no_greedy': Value('null'), 'sampling': Value('null'), 'belief_sampling': Value('string'), 'temperature': Value('float64'), 'top_p': Value('null'), 'top_k': Value('null'), 'min_p': Value('null'), 'n_samples': Value('int64'), 'max_new_tokens': Value('int64'), 'use_8bit': Value('bool'), 'batch_size': Value('null'), 'max_input_tokens': Value('int64'), 'api_processing': Value('string'), 'assistant_model': Value('string'), 'user_model': Value('string'), 'generate_fn': Value('string')}, 'generations': {'claim_variation': {'n_distinct_claims': Value('int64'), 'n_clarifying': Value('int64'), 'n_refusing': Value('int64'), 'claims': List({'label': Value('string'), 'representative': Value('string'), 'n_samples': Value('int64'), 'sample_indices': List(Value('int64')), 'type': Value('string')}), 'raw_response': Value('string')}}, 'context': {'question': Value('string'), 'n_belief_samples': Value('int64')}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
id: string
generation_config: struct<model_name: string, mode: string, input_path: string, output_dir: string, output_fname: null, (... 556 chars omitted)
child 0, model_name: string
child 1, mode: string
child 2, input_path: string
child 3, output_dir: string
child 4, output_fname: null
child 5, max_examples: null
child 6, filter_conflicts: bool
child 7, data_seed: int64
child 8, direct_prompt: string
child 9, reasoner_prompt: string
child 10, final_prompt: null
child 11, no_greedy: null
child 12, sampling: string
child 13, belief_sampling: string
child 14, temperature: null
child 15, top_p: null
child 16, top_k: null
child 17, min_p: null
child 18, n_samples: int64
child 19, max_new_tokens: int64
child 20, use_8bit: bool
child 21, batch_size: null
child 22, max_input_tokens: int64
child 23, api_processing: string
child 24, assistant_model: string
child 25, user_model: null
child 26, generate_fn: string
child 27, resolved_generation_params: struct<n_samples: int64, max_new_tokens: int64, api_processing: string, temperature: null>
child 0, n_samples: int64
child 1, max_new_tokens: int64
child 2, api_processing: string
child 3, temperature: null
generations: struct<strategy: string, reasoning: string, summary: null, response: string, raw_response: string>
child 0, strategy: string
child 1, reasoning: string
child 2, summary: null
child 3, response: string
child 4, raw_response: string
prompt: list<item: struct<role: string, content: string>>
child 0, item: struct<role: string, content: string>
child 0, role: string
child 1, content: string
processed_prompt: list<item: struct<role: string, content: string>>
child 0, item: struct<role: string, content: string>
child 0, role: string
child 1, content: string
context: struct<question: string, belief_state: list<item: string>, references: list<item: list<item: string> (... 110 chars omitted)
child 0, question: string
child 1, belief_state: list<item: string>
child 0, item: string
child 2, references: list<item: list<item: string>>
child 0, item: list<item: string>
child 0, item: string
child 3, disambiguations: list<item: struct<question: string, answer: list<item: string>, annotation_index: int64>>
child 0, item: struct<question: string, answer: list<item: string>, annotation_index: int64>
child 0, question: string
child 1, answer: list<item: string>
child 0, item: string
child 2, annotation_index: int64
to
{'id': Value('string'), 'generation_config': {'model_name': Value('string'), 'mode': Value('string'), 'input_path': Value('string'), 'output_dir': Value('string'), 'output_fname': Value('string'), 'max_examples': Value('null'), 'filter_conflicts': Value('bool'), 'data_seed': Value('int64'), 'direct_prompt': Value('string'), 'reasoner_prompt': Value('string'), 'final_prompt': Value('null'), 'no_greedy': Value('null'), 'sampling': Value('null'), 'belief_sampling': Value('string'), 'temperature': Value('float64'), 'top_p': Value('null'), 'top_k': Value('null'), 'min_p': Value('null'), 'n_samples': Value('int64'), 'max_new_tokens': Value('int64'), 'use_8bit': Value('bool'), 'batch_size': Value('null'), 'max_input_tokens': Value('int64'), 'api_processing': Value('string'), 'assistant_model': Value('string'), 'user_model': Value('string'), 'generate_fn': Value('string')}, 'generations': {'claim_variation': {'n_distinct_claims': Value('int64'), 'n_clarifying': Value('int64'), 'n_refusing': Value('int64'), 'claims': List({'label': Value('string'), 'representative': Value('string'), 'n_samples': Value('int64'), 'sample_indices': List(Value('int64')), 'type': Value('string')}), 'raw_response': Value('string')}}, 'context': {'question': Value('string'), 'n_belief_samples': Value('int64')}}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id string | generation_config dict | generations dict | context dict |
|---|---|---|---|
-7627753863503160582 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
-142049894279719726 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
2872295608752061902 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
-1327179339177796042 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
-7234567131490543978 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
8989950493209334434 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
5030890986767649661 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
-1989539151977634744 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
4588786007774587117 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
-6831167836353165517 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
7497487927888236066 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
-841558448401001630 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
2187765416989332036 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": {
"n_distinct_claims": 1,
"n_clarifying": 0,
"n_refusing": 0,
"claims": [
{
"label": "A",
"representative": "Robert Wadlow",
"n_samples": 10,
"sample_indices": [
1,
2,
3,
4,
5,
6,
... | {
"question": "Who is the tallest person on the earth?",
"n_belief_samples": 10
} |
-8711923978613103801 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
2222770043130700204 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
9137853598452853384 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
-3421486869502537513 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
-2902127503107129011 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
-8427560235042270416 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
6692449947618501059 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
-2893689352564119139 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
8902104855469910963 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
-8441809914251919864 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
-3006995445128823567 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
955636187401991675 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
"input_path": "./data/generations/dev/final_gemini-2.5-flash_gemini-2.5-flash_50_belief_belief.jsonl",
"output_dir": "./data/generations/dev",
"output_fname": "claim_variation_final_gemini-2.5-flash_gemini-2.5-flash_gemini-2.5-flash_50_belie... | {
"claim_variation": null
} | {
"question": "",
"n_belief_samples": 0
} |
5780388869788119926 | {
"model_name": "gemini-2.5-flash",
"mode": "claim_variation_final",
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End of preview.
Belief-Augmented Generation — Data
Data, generations, and evaluations from the paper: Clarify, Abstain or Answer? Strategising in Conversation with Belief-AugmentedGeneration (to appear at INLG 2026).
Built on AmbigQA (light split, Min et al. 2020, CC BY-SA 3.0).
- Paper: https://arxiv.org/pdf/2605.25831
- Code: https://github.com/jsbaan/belief-augmented-generation
- Interactive demo: https://jorisbaan.nl/belief-augmented-generation
License
CC BY-SA 3.0, inherited from AmbigQA.
Citation
If you use this data, please cite the paper (see repo for details).
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