BBH

Overview

BBH (BIG-Bench Hard) is a subset of 23 challenging tasks from the BIG-Bench benchmark that are specifically selected because language models initially struggled with them. These tasks require complex reasoning abilities that benefit from Chain-of-Thought (CoT) prompting.

Task Description

Task Type: Mixed (Multiple-Choice and Free-Form)
Input: Task-specific questions requiring reasoning
Output: Answers in specified format
Subsets: 27 reasoning tasks divided into multiple-choice (17) and free-form (10)

Key Features

27 challenging reasoning tasks from BIG-Bench
Multiple-choice tasks: temporal sequences, disambiguation, logical deduction, etc.
Free-form tasks: arithmetic, navigation, boolean expressions, etc.
Each task comes with curated Chain-of-Thought examples
Designed to test advanced reasoning capabilities

Evaluation Notes

Default configuration uses 3-shot with CoT prompting (recommended)
CoT prompts are pre-defined for each subset in cot_prompts/ directory
Answers should follow the format: "So the answer is [ANSWER]"
Setting few_shot_num=0 disables few-shot examples
Multiple-choice answers are normalized to single letters (A, B, C, etc.)

Properties

Property	Value
Benchmark Name	`bbh`
Dataset ID	evalscope/bbh
Paper	N/A
Tags	`Reasoning`
Metrics	`acc`
Default Shots	3-shot
Evaluation Split	`test`

Data Statistics

Metric	Value
Total Samples	6,511
Prompt Length (Mean)	3307.29 chars
Prompt Length (Min/Max)	1060 / 7885 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
`temporal_sequences`	250	3746.18	3646	3876
`disambiguation_qa`	250	4047.48	3993	4099
`date_understanding`	250	1550.66	1491	1641
`tracking_shuffled_objects_three_objects`	250	3257.42	3195	3316
`penguins_in_a_table`	146	3030.88	2922	3201
`geometric_shapes`	250	5270.24	5201	5384
`snarks`	178	3493.68	3339	3693
`ruin_names`	250	3832.01	3781	3948
`tracking_shuffled_objects_seven_objects`	250	3598.1	3506	3682
`tracking_shuffled_objects_five_objects`	250	3419.36	3338	3489
`logical_deduction_three_objects`	250	3093.32	3014	3165
`hyperbaton`	250	3433.3	3386	3486
`logical_deduction_five_objects`	250	3264.38	3118	3379
`logical_deduction_seven_objects`	250	3434.09	3217	3633
`movie_recommendation`	250	2489.85	2436	2613
`salient_translation_error_detection`	250	7401.64	7223	7885
`reasoning_about_colored_objects`	250	2818.32	2572	3102
`multistep_arithmetic_two`	250	2596.98	2594	2600
`navigate`	250	2508.7	2452	2626
`dyck_languages`	250	2723.8	2680	2874
`word_sorting`	250	2481.34	2397	2569
`sports_understanding`	250	1077.42	1060	1122
`boolean_expressions`	250	1991.7	1980	1998
`object_counting`	250	1706.66	1647	1787
`formal_fallacies`	250	5185.5	4918	5514
`causal_judgement`	187	4877.42	4194	6311
`web_of_lies`	250	3300.84	3267	3340

Sample Example

Subset: temporal_sequences

{
  "input": [
    {
      "id": "7d1767c8",
      "content": "Task description: Answer questions about which times certain events could have occurred.\n\nQ: Today, Emily went to the museum. Between what times could they have gone?\nWe know that:\nEmily woke up at 1pm.\nElizabeth saw Emily reading at the libr ... [TRUNCATED] ... \nOptions:\n(A) 6pm to 9pm\n(B) 7am to 11am\n(C) 1pm to 2pm\n(D) 2pm to 6pm\nA: Let's think step by step. Put your final answer in the format of \"So the answer is [ANSWER]\" (without quotes and markdown) where [ANSWER] is the answer to the problem.\n"
    }
  ],
  "target": "A",
  "id": 0,
  "group_id": 0,
  "subset_key": "temporal_sequences",
  "metadata": {
    "task_type": "multiple_choice"
  }
}

Note: Some content was truncated for display.

Prompt Template

Prompt Template:

Q: {question}
A: Let's think step by step. Put your final answer in the format of "So the answer is [ANSWER]" (without quotes and markdown) where [ANSWER] is the answer to the problem.

Few-shot Template

{fewshot}

Q: {question}
A: Let's think step by step. Put your final answer in the format of "So the answer is [ANSWER]" (without quotes and markdown) where [ANSWER] is the answer to the problem.

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets bbh \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['bbh'],
    dataset_args={
        'bbh': {
            # subset_list: ['temporal_sequences', 'disambiguation_qa', 'date_understanding']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)

5.4 KiB Raw Permalink Blame History

BBH