SIQA

Overview

SIQA (Social Interaction QA) is a benchmark for evaluating social commonsense intelligence - understanding people's actions and their social implications. Unlike benchmarks focusing on physical knowledge, SIQA tests reasoning about human behavior.

Task Description

Task Type: Social Commonsense Reasoning
Input: Context about a social situation with question and 3 answer choices
Output: Most socially appropriate answer (A, B, or C)
Focus: Human behavior, motivations, and social implications

Key Features

Tests social intelligence and emotional understanding
Questions about people's actions and their consequences
Covers motivations, reactions, and social norms
33K+ crowdsourced QA pairs
Requires reasoning about human psychology

Evaluation Notes

Default configuration uses 0-shot evaluation
Uses simple multiple-choice prompting
Evaluates on validation split
Simple accuracy metric

Properties

Property	Value
Benchmark Name	`siqa`
Dataset ID	extraordinarylab/siqa
Paper	N/A
Tags	`Commonsense`, `MCQ`, `Reasoning`
Metrics	`acc`
Default Shots	0-shot
Evaluation Split	`validation`

Data Statistics

Metric	Value
Total Samples	1,954
Prompt Length (Mean)	289.05 chars
Prompt Length (Min/Max)	242 / 509 chars

Sample Example

Subset: default

{
  "input": [
    {
      "id": "8d09aab2",
      "content": "Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C.\n\nWhat does Tracy need to do before this?\n\nA) make a new plan\nB) Go home and see Riley\nC) Find somewhere to go"
    }
  ],
  "choices": [
    "make a new plan",
    "Go home and see Riley",
    "Find somewhere to go"
  ],
  "target": "C",
  "id": 0,
  "group_id": 0,
  "metadata": {}
}

Prompt Template

Prompt Template:

Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}.

{question}

{choices}

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets siqa \
    --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=['siqa'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)

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