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 |
|
Dataset ID |
|
Paper |
N/A |
Tags |
|
Metrics |
|
Default Shots |
0-shot |
Evaluation Split |
|
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)