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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'response_filtered'})
This happened while the json dataset builder was generating data using
hf://datasets/ceselder/cot-oracle-answer-trajectory/answer_trajectory_cleaned.jsonl (at revision f3894df13f791d79cd2f796485823020951f0157), [/tmp/hf-datasets-cache/medium/datasets/28683907771477-config-parquet-and-info-ceselder-cot-oracle-answe-c090a53b/hub/datasets--ceselder--cot-oracle-answer-trajectory/snapshots/f3894df13f791d79cd2f796485823020951f0157/answer_trajectory.jsonl (origin=hf://datasets/ceselder/cot-oracle-answer-trajectory@f3894df13f791d79cd2f796485823020951f0157/answer_trajectory.jsonl), /tmp/hf-datasets-cache/medium/datasets/28683907771477-config-parquet-and-info-ceselder-cot-oracle-answe-c090a53b/hub/datasets--ceselder--cot-oracle-answer-trajectory/snapshots/f3894df13f791d79cd2f796485823020951f0157/answer_trajectory_cleaned.jsonl (origin=hf://datasets/ceselder/cot-oracle-answer-trajectory@f3894df13f791d79cd2f796485823020951f0157/answer_trajectory_cleaned.jsonl)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
datapoint_type: string
prompt: string
target_response: string
layer: int64
layers: list<item: int64>
child 0, item: int64
num_positions: int64
context_input_ids: list<item: int64>
child 0, item: int64
context_positions: list<item: int64>
child 0, item: int64
sent_idx: int64
total_sentences: int64
pct: int64
final_answer: string
response_filtered: string
to
{'datapoint_type': Value('string'), 'prompt': Value('string'), 'target_response': Value('string'), 'layer': Value('int64'), 'layers': List(Value('int64')), 'num_positions': Value('int64'), 'context_input_ids': List(Value('int64')), 'context_positions': List(Value('int64')), 'sent_idx': Value('int64'), 'total_sentences': Value('int64'), 'pct': Value('int64'), 'final_answer': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1889, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'response_filtered'})
This happened while the json dataset builder was generating data using
hf://datasets/ceselder/cot-oracle-answer-trajectory/answer_trajectory_cleaned.jsonl (at revision f3894df13f791d79cd2f796485823020951f0157), [/tmp/hf-datasets-cache/medium/datasets/28683907771477-config-parquet-and-info-ceselder-cot-oracle-answe-c090a53b/hub/datasets--ceselder--cot-oracle-answer-trajectory/snapshots/f3894df13f791d79cd2f796485823020951f0157/answer_trajectory.jsonl (origin=hf://datasets/ceselder/cot-oracle-answer-trajectory@f3894df13f791d79cd2f796485823020951f0157/answer_trajectory.jsonl), /tmp/hf-datasets-cache/medium/datasets/28683907771477-config-parquet-and-info-ceselder-cot-oracle-answe-c090a53b/hub/datasets--ceselder--cot-oracle-answer-trajectory/snapshots/f3894df13f791d79cd2f796485823020951f0157/answer_trajectory_cleaned.jsonl (origin=hf://datasets/ceselder/cot-oracle-answer-trajectory@f3894df13f791d79cd2f796485823020951f0157/answer_trajectory_cleaned.jsonl)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
datapoint_type string | prompt string | target_response string | layer int64 | layers list | num_positions int64 | context_input_ids list | context_positions list | sent_idx int64 | total_sentences int64 | pct int64 | final_answer string |
|---|---|---|---|---|---|---|---|---|---|---|---|
cot_answer_trajectory | Activations from 33 positions across layers 9, 18, 27, representing sentence 2/149 (1%) of the model's chain of thought. What does the model currently think the answer is? | one of the options A to E. So, I need to figure out the relationship between 34 and 43, and then apply the same relationship to 25 to find the correct answer | 9 | [
9,
18,
27
] | 33 | [
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65,
70,
71,
8,
16,
25,
33,
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50,
55,
60,
65,
70,
71
] | 1 | 149 | 1 | B |
cot_answer_trajectory | Activations from 57 positions across layers 9, 18, 27, representing sentence 3/149 (2%) of the model's chain of thought. What does the model currently think the answer is? | one of the options A to E. Let me think about possible relationships between the numbers | 9 | [
9,
18,
27
] | 57 | [
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18,
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... | 2 | 149 | 2 | B |
cot_answer_trajectory | Activations from 72 positions across layers 9, 18, 27, representing sentence 4/149 (2%) of the model's chain of thought. What does the model currently think the answer is? | either A, B, C, D, or E. Let me think about how 34 relates to 43. Maybe there's a mathematical operation that transforms 34 into 43. Let me check the difference first | 9 | [
9,
18,
27
] | 72 | [
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8,
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2... | 3 | 149 | 2 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 5/149 (3%) of the model's chain of thought. What does the model currently think the answer is? | likely to be one of the options given. Let me think about possible relationships | 9 | [
9,
18,
27
] | 75 | [
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... | 4 | 149 | 3 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 6/149 (4%) of the model's chain of thought. What does the model currently think the answer is? | C)36. But wait, let me check again. Maybe there's another pattern | 9 | [
9,
18,
27
] | 75 | [
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145,
... | 5 | 149 | 4 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 7/149 (4%) of the model's chain of thought. What does the model currently think the answer is? | \boxed{C} | 9 | [
9,
18,
27
] | 75 | [
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145,
... | 6 | 149 | 4 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 8/149 (5%) of the model's chain of thought. What does the model currently think the answer is? | 43 - 34 = 9. So, the difference is 9. But then, if I apply that to 25, adding 9 would give 34. But 34 isn't one of the options | 9 | [
9,
18,
27
] | 75 | [
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115,
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130,
135,
140,
145,
... | 7 | 149 | 5 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 9/149 (6%) of the model's chain of thought. What does the model currently think the answer is? | 25 + 9 = 34? Wait, but that would be 34, but 34 is already in the original pair. That doesn't make sense. Maybe not the difference | 9 | [
9,
18,
27
] | 75 | [
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130,
135,
140,
145,
... | 8 | 149 | 6 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 10/149 (6%) of the model's chain of thought. What does the model currently think the answer is? | 25 plus 9, which is 34? Wait, but 34 is already in the original pair. That doesn't make sense. Maybe not the difference | 9 | [
9,
18,
27
] | 75 | [
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145,
... | 9 | 149 | 6 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 11/149 (7%) of the model's chain of thought. What does the model currently think the answer is? | 25 + 9 = 34? But 34 isn't one of the options. Wait, maybe I got the direction wrong. Maybe it's not adding 9, but something else | 9 | [
9,
18,
27
] | 75 | [
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... | 10 | 149 | 7 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 12/149 (8%) of the model's chain of thought. What does the model currently think the answer is? | 34? But 34 isn't one of the options. Wait, the options are A)49, B)52, C)36, D)64, E)56. So that can't be | 9 | [
9,
18,
27
] | 75 | [
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... | 11 | 149 | 8 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 13/149 (8%) of the model's chain of thought. What does the model currently think the answer is? | not 34. So maybe that's not the right approach | 9 | [
9,
18,
27
] | 75 | [
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130,
135,
140,
145,
... | 12 | 149 | 8 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 14/149 (9%) of the model's chain of thought. What does the model currently think the answer is? | not 34. So maybe that's not the right approach | 9 | [
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] | 75 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 15/149 (10%) of the model's chain of thought. What does the model currently think the answer is? | C)36. Wait, but let me check again. Maybe there's another relationship. Let me think about the digits. 34 and 43. If you reverse the digits of 34, you get 43 | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 16/149 (10%) of the model's chain of thought. What does the model currently think the answer is? | C)36. Wait, but why? | 9 | [
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... | 15 | 149 | 10 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 17/149 (11%) of the model's chain of thought. What does the model currently think the answer is? | 36. Wait, but why? Let me check | 9 | [
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... | 16 | 149 | 11 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 18/149 (12%) of the model's chain of thought. What does the model currently think the answer is? | the reverse of 25. Let's check: 25 reversed is 52. And 52 is option B. That seems possible. But let me check if there's another pattern | 9 | [
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27
] | 75 | [
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... | 17 | 149 | 12 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 19/149 (12%) of the model's chain of thought. What does the model currently think the answer is? | the reverse of 25. Let's check: 25 reversed is 52. And 52 is option B. So maybe the answer is B)52? | 9 | [
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... | 18 | 149 | 12 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 20/149 (13%) of the model's chain of thought. What does the model currently think the answer is? | the reverse of 25. Let's check: 25 reversed is 52. And 52 is option B. So maybe the answer is B)52? | 9 | [
9,
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] | 75 | [
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... | 19 | 149 | 13 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 21/149 (14%) of the model's chain of thought. What does the model currently think the answer is? | the reverse of 25. Let's check: 25 reversed is 52. And 52 is option B. So maybe the answer is B)52 | 9 | [
9,
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] | 75 | [
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... | 20 | 149 | 14 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 22/149 (14%) of the model's chain of thought. What does the model currently think the answer is? | B)52. Let me check if that makes sense | 9 | [
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] | 75 | [
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... | 21 | 149 | 14 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 23/149 (15%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But wait, let me make sure there isn't another possible relationship | 9 | [
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18,
27
] | 75 | [
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135,
140,
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... | 22 | 149 | 15 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 24/149 (16%) of the model's chain of thought. What does the model currently think the answer is? | B? | 9 | [
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] | 75 | [
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... | 23 | 149 | 16 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 25/149 (16%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check if there's another possible relationship to make sure | 9 | [
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18,
27
] | 75 | [
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135,
140,
145,
... | 24 | 149 | 16 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 26/149 (17%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But wait, let me check if there's another possible relationship. For example, maybe adding the digits? Let's see. 3 + 4 = 7, and 4 + 3 = 7. So | 9 | [
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135,
140,
145,
... | 25 | 149 | 17 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 27/149 (18%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again. Let me think if there's another possible relationship. For example, maybe adding the digits? 3 + 4 = 7, and 4 + 3 = 7. So the | 9 | [
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18,
27
] | 75 | [
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135,
140,
145,
... | 26 | 149 | 18 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 28/149 (18%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again. Let me think of other possibilities | 9 | [
9,
18,
27
] | 75 | [
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130,
135,
140,
145,
... | 27 | 149 | 18 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 29/149 (19%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again. Let me think if there's another possible relationship | 9 | [
9,
18,
27
] | 75 | [
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130,
135,
140,
145,
... | 28 | 149 | 19 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 30/149 (20%) of the model's chain of thought. What does the model currently think the answer is? | a number whose digits add up to 7? But 25's digits add up to 7 as well (2 + 5 = 7). So then, the answer would be a number with digits adding to 7. Let's | 9 | [
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18,
27
] | 75 | [
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... | 29 | 149 | 20 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 31/149 (20%) of the model's chain of thought. What does the model currently think the answer is? | a number whose digits add up to 7. Let's check the options | 9 | [
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18,
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... | 30 | 149 | 20 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 32/149 (21%) of the model's chain of thought. What does the model currently think the answer is? | a number where the digits add up to 7. Let's check the options: | 9 | [
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18,
27
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... | 31 | 149 | 21 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 33/149 (22%) of the model's chain of thought. What does the model currently think the answer is? | a number whose digits add up to 7. Let's check the options: | 9 | [
9,
18,
27
] | 75 | [
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... | 32 | 149 | 22 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 34/149 (22%) of the model's chain of thought. What does the model currently think the answer is? | a number whose digits add up to 7. Let's check the options: | 9 | [
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18,
27
] | 75 | [
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... | 33 | 149 | 22 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 35/149 (23%) of the model's chain of thought. What does the model currently think the answer is? | B)52 | 9 | [
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18,
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... | 34 | 149 | 23 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 36/149 (24%) of the model's chain of thought. What does the model currently think the answer is? | B)52 | 9 | [
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18,
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... | 35 | 149 | 24 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 37/149 (24%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check if there's another possible relationship | 9 | [
9,
18,
27
] | 75 | [
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135,
140,
145,
... | 36 | 149 | 24 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 38/149 (25%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again if there's another possible relationship | 9 | [
9,
18,
27
] | 75 | [
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... | 37 | 149 | 25 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 39/149 (26%) of the model's chain of thought. What does the model currently think the answer is? | B)52? But let me check again | 9 | [
9,
18,
27
] | 75 | [
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135,
140,
145,
... | 38 | 149 | 26 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 40/149 (26%) of the model's chain of thought. What does the model currently think the answer is? | B)52? But let me check again | 9 | [
9,
18,
27
] | 75 | [
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125,
130,
135,
140,
145,
... | 39 | 149 | 26 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 41/149 (27%) of the model's chain of thought. What does the model currently think the answer is? | 52, which is B. But let me check again if there's another possible relationship | 9 | [
9,
18,
27
] | 75 | [
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130,
135,
140,
145,
... | 40 | 149 | 27 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 42/149 (28%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But wait, let me check if there's another possible relationship | 9 | [
9,
18,
27
] | 75 | [
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105,
110,
115,
120,
125,
130,
135,
140,
145,
... | 41 | 149 | 28 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 43/149 (28%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But wait, earlier I thought reversing the digits would also give 52. So both the digit sum and reversing the digits lead to 52. So that's a good sign. Let me check if there's another | 9 | [
9,
18,
27
] | 75 | [
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135,
140,
145,
... | 42 | 149 | 28 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 44/149 (29%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again if there's another possible relationship. For example, maybe 34 and 43 are related by some mathematical operation. Let me think. 34 + 9 = 43 | 9 | [
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18,
27
] | 75 | [
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125,
130,
135,
140,
145,
... | 43 | 149 | 29 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 45/149 (30%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again | 9 | [
9,
18,
27
] | 75 | [
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145,
... | 44 | 149 | 30 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 46/149 (30%) of the model's chain of thought. What does the model currently think the answer is? | B)52 | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 47/149 (31%) of the model's chain of thought. What does the model currently think the answer is? | B)52 | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 48/149 (32%) of the model's chain of thought. What does the model currently think the answer is? | B)52 | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 49/149 (32%) of the model's chain of thought. What does the model currently think the answer is? | B)52. Let me check again. Let me think if there's another possible relationship. For example, maybe multiplying digits? 3*4=12, 4*3=12. So same product. Then for | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 50/149 (33%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 51/149 (34%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 52/149 (34%) of the model's chain of thought. What does the model currently think the answer is? | 25 + 9 = 34, but 34 isn't an option. Wait, but maybe the difference is 9, but then 25 + 9 = 34, which is not an option. Alternatively | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 53/149 (35%) of the model's chain of thought. What does the model currently think the answer is? | 25 + 9 = 34. But 34 isn't an option. Wait, but maybe the difference is 9, but the next number is 25 + something else? | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 54/149 (36%) of the model's chain of thought. What does the model currently think the answer is? | 34? But 34 isn't an option. Wait, but the options are A)49, B)52, C)36, D)64, E)56. So that doesn't fit | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 55/149 (36%) of the model's chain of thought. What does the model currently think the answer is? | not that. Alternatively, maybe multiplying? 34 * something equals 43? 43 divided by 34 is roughly 1.26, which doesn't seem helpful. Maybe squares or cubes? 34 is not | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 56/149 (37%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 57/149 (38%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 58/149 (38%) of the model's chain of thought. What does the model currently think the answer is? | not straightforward. Alternatively, maybe squares or cubes? | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 59/149 (39%) of the model's chain of thought. What does the model currently think the answer is? | not straightforward. But since reversing digits gives 52, which is an option, and the sum of digits also gives 52, that seems to be the answer | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 60/149 (40%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 61/149 (40%) of the model's chain of thought. What does the model currently think the answer is? | likely B)52. But let me check again. Let me think if there's another possible relationship. For example, maybe squares or cubes? 34 is not a square, 43 isn't either. 25 is | 9 | [
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... | 60 | 149 | 40 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 62/149 (41%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 63/149 (42%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 64/149 (42%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 65/149 (43%) of the model's chain of thought. What does the model currently think the answer is? | likely B)52, since reversing the digits of 34 gives 43, and reversing 25 gives 52. Also, the sum of digits is same. So both patterns lead to B. So I think the answer | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 66/149 (44%) of the model's chain of thought. What does the model currently think the answer is? | 52? Because reversing digits gives 52, and digits sum to 7. So both patterns point to B)52. Let me check the options again. The options are A)49, B)52, C | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 67/149 (44%) of the model's chain of thought. What does the model currently think the answer is? | 36 (6 squared) or 49 (7 squared) or 64 (8 squared). Let's check the options. C)36, A)49, D)64. But 25 is | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 68/149 (45%) of the model's chain of thought. What does the model currently think the answer is? | 52? Wait, 52 is not a square. 49 is 7 squared, which is option A. 64 is 8 squared, option D. So if the relationship is that 25 is a square | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 69/149 (46%) of the model's chain of thought. What does the model currently think the answer is? | 49? But then, how does that relate to 34 and 43? | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 70/149 (46%) of the model's chain of thought. What does the model currently think the answer is? | either A or C. But earlier, we saw that B)52 is the answer if we reverse digits or sum digits. So which is it? | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 71/149 (47%) of the model's chain of thought. What does the model currently think the answer is? | one of those. But 25 is 5 squared. If the relationship is square numbers, then maybe 25 is 5², and the next square is 6²=36 (option C) or 7²= | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 72/149 (48%) of the model's chain of thought. What does the model currently think the answer is? | 7 squared (49), 6 squared (36), 8 squared (64), etc. But how does that relate to 34 and 43? | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 73/149 (48%) of the model's chain of thought. What does the model currently think the answer is? | 49? But then why is 34 and 43 related? Maybe not. Alternatively, maybe the numbers are related to their squares. Let me think. 34 and 43. 34 is 5^ | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 74/149 (49%) of the model's chain of thought. What does the model currently think the answer is? | not directly about squares. Unless there's another pattern | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 75/149 (50%) of the model's chain of thought. What does the model currently think the answer is? | B)52, since reversing digits and sum of digits both lead to that. Let me check again. If the relationship is reversing digits, then 34 becomes 43, so 25 becomes 52. If the relationship | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 76/149 (51%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 77/149 (51%) of the model's chain of thought. What does the model currently think the answer is? | B)52. Because reversing digits gives 52, and the sum of digits is same. So I think B is the answer. Let me check again | 9 | [
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... | 76 | 149 | 51 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 78/149 (52%) of the model's chain of thought. What does the model currently think the answer is? | likely B)52, since reversing the digits of 34 gives 43, and reversing 25 gives 52. Also, the sum of digits is same. So both patterns lead to B. So I think the answer | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 79/149 (53%) of the model's chain of thought. What does the model currently think the answer is? | B)52, since reversing the digits of 34 gives 43, and reversing 25 gives 52. Also, the sum of digits is same. So both patterns lead to B. So I think the answer is | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 80/149 (53%) of the model's chain of thought. What does the model currently think the answer is? | B)52, since reversing the digits of 34 gives 43, and reversing 25 gives 52. Also, the sum of digits is same. So both patterns lead to B. So I think the answer is | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 81/149 (54%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check if there's another possible relationship | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 82/149 (55%) of the model's chain of thought. What does the model currently think the answer is? | B)52 | 9 | [
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... | 81 | 149 | 55 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 83/149 (55%) of the model's chain of thought. What does the model currently think the answer is? | B)52 | 9 | [
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... | 82 | 149 | 55 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 84/149 (56%) of the model's chain of thought. What does the model currently think the answer is? | B | 9 | [
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... | 83 | 149 | 56 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 85/149 (57%) of the model's chain of thought. What does the model currently think the answer is? | B)52 | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 86/149 (57%) of the model's chain of thought. What does the model currently think the answer is? | B)52 | 9 | [
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... | 85 | 149 | 57 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 87/149 (58%) of the model's chain of thought. What does the model currently think the answer is? | B)52 | 9 | [
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... | 86 | 149 | 58 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 88/149 (59%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again | 9 | [
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... | 87 | 149 | 59 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 89/149 (59%) of the model's chain of thought. What does the model currently think the answer is? | 25 + 9 = 34, but 34 isn't an option. Wait, but maybe the difference is 9, but in another way. For example, 34 + 9 = 43. If | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 90/149 (60%) of the model's chain of thought. What does the model currently think the answer is? | 34? But 34 isn't an option. Wait, but maybe the difference is multiplied? Or something else. Alternatively, maybe the difference between the digits? Let's see, 34: 4 - 3 = | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 91/149 (61%) of the model's chain of thought. What does the model currently think the answer is? | not that. Alternatively, maybe 34 + 43 = 77. Not sure. 25 + ? = something? Not sure | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 92/149 (61%) of the model's chain of thought. What does the model currently think the answer is? | B)52. So I think the answer is B | 9 | [
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... | 91 | 149 | 61 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 93/149 (62%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again | 9 | [
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... | 92 | 149 | 62 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 94/149 (63%) of the model's chain of thought. What does the model currently think the answer is? | 25 + 9 = 34, but that's not an option. Wait, but maybe the difference is 9, but in the other direction? 34 + 9 = 43. So if we take | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 95/149 (63%) of the model's chain of thought. What does the model currently think the answer is? | B)52 | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 96/149 (64%) of the model's chain of thought. What does the model currently think the answer is? | B)52. Because reversing the digits of 34 gives 43, and reversing 25 gives 52. Also, the sum of digits is same. So both patterns point to B. So I think the answer is | 9 | [
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... | 95 | 149 | 64 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 97/149 (65%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again | 9 | [
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cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 98/149 (65%) of the model's chain of thought. What does the model currently think the answer is? | B)52. But let me check again. If the relationship is reversing digits, then 25 becomes 52. If the relationship is digit sum, then 25 (sum 7) needs a number with sum 7 | 9 | [
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... | 97 | 149 | 65 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 99/149 (66%) of the model's chain of thought. What does the model currently think the answer is? | B)52. Since both the digit reversal and digit sum match. So I think the answer is B | 9 | [
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... | 98 | 149 | 66 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 100/149 (67%) of the model's chain of thought. What does the model currently think the answer is? | 52, which is 5 and 2, adding to 7. So that's the same as the sum of digits. So that's another way to think about it | 9 | [
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... | 99 | 149 | 67 | B |
cot_answer_trajectory | Activations from 75 positions across layers 9, 18, 27, representing sentence 101/149 (67%) of the model's chain of thought. What does the model currently think the answer is? | a number whose digits add to 7. Which is 52. So that's B | 9 | [
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... | 100 | 149 | 67 | B |
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