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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 4 new columns ({'final_answer', 'sent_idx', 'pct', 'total_sentences'})
This happened while the json dataset builder was generating data using
hf://datasets/japhba/cot-oracle-training-v6/answer_trajectory.jsonl (at revision da6bdd567ea28c713a546c18dfaf49f296f1c6cf), [/tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/answer_pred.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/answer_pred.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/answer_trajectory.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/answer_trajectory.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/compqa.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/compqa.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/conv_qa.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/conv_qa.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/correctness.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/correctness.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/decorative.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/decorative.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/domain.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/domain.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/full_recon.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/full_recon.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/load_bearing.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/load_bearing.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/manifest.json (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/manifest.json), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/next_step.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/next_step.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/partial_answer.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/partial_answer.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/reasoning_term.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/reasoning_term.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
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'))}
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 4 new columns ({'final_answer', 'sent_idx', 'pct', 'total_sentences'})
This happened while the json dataset builder was generating data using
hf://datasets/japhba/cot-oracle-training-v6/answer_trajectory.jsonl (at revision da6bdd567ea28c713a546c18dfaf49f296f1c6cf), [/tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/answer_pred.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/answer_pred.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/answer_trajectory.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/answer_trajectory.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/compqa.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/compqa.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/conv_qa.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/conv_qa.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/correctness.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/correctness.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/decorative.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/decorative.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/domain.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/domain.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/full_recon.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/full_recon.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/load_bearing.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/load_bearing.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/manifest.json (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/manifest.json), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/next_step.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/next_step.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/partial_answer.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/partial_answer.jsonl), /tmp/hf-datasets-cache/medium/datasets/94579216805436-config-parquet-and-info-japhba-cot-oracle-trainin-9e67138b/hub/datasets--japhba--cot-oracle-training-v6/snapshots/da6bdd567ea28c713a546c18dfaf49f296f1c6cf/reasoning_term.jsonl (origin=hf://datasets/japhba/cot-oracle-training-v6@da6bdd567ea28c713a546c18dfaf49f296f1c6cf/reasoning_term.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 |
|---|---|---|---|---|---|---|---|
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | F | 9 | [
9,
18,
27
] | 75 | [
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59,
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1291,
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315,
330,
40645,
72,
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43,
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257,
262,
267,
272,
277,
282,
... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | B | 9 | [
9,
18,
27
] | 75 | [
151644,
872,
198,
2679,
220,
16,
20,
2953,
646,
56903,
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1939,
32,
8,
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33,
8,
17,
... | [
12,
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116,
121,
126,
131,
136,
141,
146,
151,
156,
161,
... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine Gamini's current age, we begin by defining variables:
- Let **Y** be Yamini's current age.
- Let **G** be Gamini's current age.
We are given two key pieces of information:
1. **One yea | 9 | [
9,
18,
27
] | 75 | [
151644,
872,
198,
3966,
1042,
4134,
279,
11341,
315,
33554,
6591,
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323,
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16085,
419,
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1035,
3635,
220,
22,
549,
220,
23,
13,
2585,
2310,
374,
29979,... | [
14,
28,
42,
56,
70,
84,
89,
94,
99,
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109,
114,
119,
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129,
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139,
144,
149,
154,
159,
164,
169,
... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | G | 9 | [
9,
18,
27
] | 75 | [
151644,
872,
198,
32,
45297,
30283,
374,
24109,
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264,
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255,
260,
265,
270,
275,
280,
285,
... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
9,
18,
27
] | 75 | [
151644,
872,
198,
18,
23,
23,
24,
488,
220,
16,
17,
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34,
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117,
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127,
132,
137,
142,
147,
152,
157,
162,
167,
172... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | D | 9 | [
9,
18,
27
] | 75 | [
151644,
872,
198,
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374,
264,
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126,
131,
136,
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146,
151,
156,
16... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To solve the problem, we need to interpret the given birth and death rates and relate them to the net growth rate of the population. The key is understanding what the "per certain number of people" re | 9 | [
9,
18,
27
] | 75 | [
151644,
872,
198,
785,
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4379,
817,
3654,
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18,
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | D | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | D | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | C | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine the difference between the heaviest and lightest pumpkin, we start by calculating the weights of each participant's pumpkin:
1. **Brad's pumpkin** weighs **54 pounds**.
2. **Jessica's pu | 9 | [
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18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine the size of John’s watermelon, we begin by analyzing the relationships described in the problem step by step.
---
### Step 1: Michael's Watermelon
We are told that Michael’s largest wat | 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine Janice's age, we begin by calculating the ages of Mark and Graham based on the given information.
---
### Step 1: Determine Mark's Age
- Mark was born in **January 1976**.
- The curren | 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | B | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | B | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine how much Dave invested, we analyze the profit-sharing mechanism between David and Dave based on their respective investments and the duration of their investments.
---
### **Step 1: Und | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | E | 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | C | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | E | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine which of the given options divides 48 evenly (i.e., with no remainder), we can perform division checks for each of the options:
---
### Option A: 3
Divide 48 by 3:
$$
48 \div 3 = 16 | 9 | [
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18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | B | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | E | 9 | [
9,
18,
27
] | 75 | [
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22... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To solve this problem, we are given a relationship between two groups of people in a town:
- The number of people **born in the town's state** is **two times** the number of people **born in another | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | 36 | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | I | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To solve the problem:
> **"What are the number of ways to select 3 men and 2 women such that one man and one woman are always selected?"**
We begin by interpreting the condition: "one man and one wo | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | 48 | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | C | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | C | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine how much the water level in a cylindrical tank drops when 11 liters of water is removed, we need to use the formula for the volume of a cylinder:
$$
V = \pi r^2 h
$$
Where:
- $ V $ is t | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | D | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | C | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | B | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To solve the problem, let's break it down step by step:
---
### **Step 1: Determine the total number of candies used**
Jan breaks down **3 blocks** of candy. Each block produces **30 pieces** of ca | 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To solve the problem, we define:
- $ J $: John's current age
- $ T $: Tom's current age
---
### Step 1: Translate the problem into equations
**First condition:**
"John was thrice as old as T | 9 | [
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18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | 42 | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | 7 | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
9,
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine how many people tried to park without paying, we begin by analyzing the given data and interpreting the relationships between the groups of cars.
---
### Step 1: Total number of cars
We | 9 | [
9,
18,
27
] | 75 | [
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... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine the probability that **A and C are sitting adjacent** in a row of 7 people (A, B, C, D, E, F, G), we can proceed with a structured approach:
---
### **Step 1: Total Number of Arrangemen | 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To solve the problem, we are given the following:
- A sum of money earns **simple interest** and becomes **$1400 in 2 years** and **$2000 in 5 years**.
- We are asked: **In how many years would the a | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | B | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | C | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | C | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | C | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | G | 9 | [
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18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine how much the third person will get from the profit, we begin by analyzing the distribution of the total investment.
---
### Step 1: Define the Investments
Let the first person's invest | 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | C | 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine how many kilometers need to be traveled to reach the next **palindromic** number on the car's odometer, we begin by analyzing the current odometer reading: **63736**, which is indeed a pa | 9 | [
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18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | I | 9 | [
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18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine the **least possible value of integer $ k $** such that $ k^3 $ is divisible by **168**, we begin by analyzing the prime factorization of 168.
---
### Step 1: Prime Factorization of 168 | 9 | [
9,
18,
27
] | 75 | [
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... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | C | 9 | [
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18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | E | 9 | [
9,
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To solve this problem, we need to determine the speed of a train that passes a man running in the **same direction** as the train. The key concept here is **relative speed**, since both the train and | 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | C | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To solve the problem, let’s define the positive number as $ x $. The problem states:
> Two-thirds of a positive number and $ \frac{16}{216} $ of its reciprocal are equal.
We translate this into a ma | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine how many degrees the **hour hand** of a clock rotates from **5 o'clock in the morning to 11 o'clock in the morning**, we begin by understanding the nature of the clock's movement.
---
# | 9 | [
9,
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine the number of minutes where the costs of United Telephone and Atlantic Call are the same, we begin by expressing the total cost for each company in terms of the number of minutes, denoted | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | D | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | B | 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine the percentage increase in Kathleen's weekly allowance, we begin by analyzing the given information and applying the appropriate mathematical steps.
---
### Step 1: Determine Middle Sch | 9 | [
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18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | D | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | B | 9 | [
9,
18,
27
] | 75 | [
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... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine the rate at which Bobby’s truck consumes gasoline, we need to calculate the **total miles driven** and the **total gasoline used**, then compute the **miles per gallon** (mpg) rate.
---
| 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine how many papers the student scored more than 50% in, we begin by analyzing the given data:
- The student took **five papers**, each with the **same full marks**.
- His **marks in these p | 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | F | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
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18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | F | 9 | [
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18,
27
] | 75 | [
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330,
335,
340,
... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | B | 9 | [
9,
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine how long it takes for the train to cross the man, we need to consider the **relative speed** of the train and the man moving in **opposite directions**.
---
### Step 1: Determine the Re | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | C | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | 780 | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | 30 | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To solve the problem of finding how many arrangements of the letters in the word **"MEADOWS"** can be made such that the **vowels occupy the even positions**, we proceed step by step.
---
### Step 1 | 9 | [
9,
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
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27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | B | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To simplify the expression:
$$
\frac{3}{\sqrt{27}}
$$
we begin by simplifying the square root in the denominator. Recognize that:
$$
\sqrt{27} = \sqrt{9 \cdot 3} = \sqrt{9} \cdot \sqrt{3} = 3\sqrt{ | 9 | [
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] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | E | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | B | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | D | 9 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | 42 | 9 | [
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18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To solve the problem, we need to understand what it means for the **minute hand of a clock to gain 5 minutes every hour**.
---
### Step 1: Determine the Time Interval
We are told that the clock sho | 9 | [
9,
18,
27
] | 75 | [
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... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine how Jones's new time compares to his old time, we analyze the relationship between **distance**, **speed**, and **time** using the formula:
$$
\text{Time} = \frac{\text{Distance}}{\text{ | 9 | [
9,
18,
27
] | 75 | [
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156,
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166,
171... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine how many bananas Donna has, we are given the following information:
- Dawn, Lydia, and Donna together have a total of **200 bananas**.
- **Dawn has 40 more bananas than Lydia**.
- **Lydi | 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | To determine the total cost of Trevor’s taxi ride downtown, including a 20% tip, we begin by analyzing the relationships between the costs of the different ride services.
---
### Step 1: Define Vari | 9 | [
9,
18,
27
] | 75 | [
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cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | J | 9 | [
9,
18,
27
] | 75 | [
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488,
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8317,
1119,
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374,
12943,
300,
16,
15,
61,
19,
... | [
26,
53,
80,
106,
133,
160,
165,
170,
175,
180,
185,
190,
195,
200,
205,
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240,
245,
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26,
53,
80,
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165,
170,
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180,
185,
190,
195,
200,
205,
210,
215,
220,
225,
230,
235,
... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | A | 9 | [
9,
18,
27
] | 75 | [
151644,
872,
198,
3838,
3267,
882,
525,
1251,
16634,
304,
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32,
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282,
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198,
35,
8,
4332,
2513,
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36,
8,
3960,
504,
1817,
100... | [
9,
18,
27,
36,
45,
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100,
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27,
36,
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65,
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75,
80,
85,
90,
95,
100,
105,
110,
115,
120,
125,
130,
135,
140,
145,
150... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | D | 9 | [
9,
18,
27
] | 75 | [
151644,
872,
198,
32,
220,
19,
17,
4666,
6284,
5220,
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279,
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13,
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220,
16,
21,
18,
9961,
320,
20,
10482,
220,
19,
304,
... | [
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93,
139,
186,
232,
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289,
294,
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329,
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284,
289,
294,
299,
304,
309,
314,
319,
324,
329,
334,
339,
344,
349,
354,
... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | 14.57 | 9 | [
9,
18,
27
] | 75 | [
151644,
872,
198,
55730,
15303,
400,
20,
18,
13,
24,
15,
13,
42920,
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400,
18,
24,
13,
18,
18,
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42920,
30,
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151644,
77091,
198,
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11,
773,
358,
1184,
311,
7071... | [
6,
12,
18,
24,
30,
36,
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46,
51,
56,
61,
66,
71,
76,
81,
86,
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101,
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126,
131,
6,
12,
18,
24,
30,
36,
41,
46,
51,
56,
61,
66,
71,
76,
81,
86,
91,
96,
101,
106,
111,
116,
121,
126,
131,
6,
... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | G | 9 | [
9,
18,
27
] | 75 | [
151644,
872,
198,
10234,
1558,
14074,
1124,
1318,
63725,
2024,
2359,
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73813,
59,
1318,
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2024,
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264,
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32,
8,
2925,
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774,
24473,
448,
23552,
198,
33,
8,
444,
531,
960,
63468,
2022,
198,
... | [
19,
38,
57,
76,
95,
115,
120,
125,
130,
135,
140,
145,
150,
155,
160,
165,
170,
175,
180,
185,
190,
195,
200,
205,
210,
19,
38,
57,
76,
95,
115,
120,
125,
130,
135,
140,
145,
150,
155,
160,
165,
170,
175,
180,
185,
190,
195,... |
cot_answer_prediction | Activations from 75 positions across layers 9, 18, 27. What is the model's final answer? | B | 9 | [
9,
18,
27
] | 75 | [
151644,
872,
198,
3379,
279,
6437,
382,
32,
8,
10799,
198,
33,
8,
2714,
12080,
198,
34,
8,
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264,
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8,
3720,
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23611,
271,
16141,
448,
279,
6524,
13,
151645,
198,
151644,
77091,
198,
32313,
11,
1077,... | [
6,
13,
20,
26,
33,
40,
45,
50,
55,
60,
65,
70,
75,
80,
85,
90,
95,
100,
105,
110,
115,
120,
125,
130,
135,
6,
13,
20,
26,
33,
40,
45,
50,
55,
60,
65,
70,
75,
80,
85,
90,
95,
100,
105,
110,
115,
120,
125,
130,
135,
6,... |
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