Multi-Choice Rubrics for Nuanced Assessment¶

Go beyond binary MET/UNMET with ordinal and nominal scales for granular evaluation.

The Scenario¶

You're evaluating restaurant reviews for a review platform. Simple "good/bad" doesn't capture the nuance—you need Likert scales for quality dimensions and categorical ratings for specific aspects. Some reviews might be excellent on detail but neutral on helpfulness.

What You'll Learn¶

Creating multi-choice criteria with CriterionOption
Ordinal vs nominal scales with scale_type
Handling NA options for inapplicable criteria
Position bias mitigation with shuffle_options
Interpreting MultiChoiceVerdict results

The Solution¶

Step 1: Define Ordinal (Likert) Criteria¶

For criteria with ordered options (1-5 scales, agreement levels):

from autorubric import Rubric, Criterion, CriterionOption

# Ordinal criterion: options have inherent order
detail_criterion = Criterion(
    name="detail_level",
    weight=10.0,
    requirement="How detailed and informative is this review?",
    scale_type="ordinal",
    options=[
        CriterionOption(label="Very Poor - No useful details", value=0.0),
        CriterionOption(label="Poor - Minimal details", value=0.25),
        CriterionOption(label="Average - Some useful details", value=0.5),
        CriterionOption(label="Good - Detailed and helpful", value=0.75),
        CriterionOption(label="Excellent - Comprehensive with specifics", value=1.0),
    ]
)

Value Assignment

Assign value explicitly (0.0 to 1.0) rather than relying on position. This avoids position bias and makes scoring intentional.

Step 2: Define Nominal (Categorical) Criteria¶

For criteria with unordered categories:

# Nominal criterion: categories without inherent order
tone_criterion = Criterion(
    name="review_tone",
    weight=5.0,
    requirement="What is the overall tone of this review?",
    scale_type="nominal",
    options=[
        CriterionOption(label="Positive", value=1.0),
        CriterionOption(label="Neutral", value=0.5),
        CriterionOption(label="Negative", value=0.0),
        CriterionOption(label="Mixed", value=0.5),
    ]
)

Step 3: Add NA Options for Inapplicable Cases¶

Some reviews may not cover certain aspects:

# Criterion with NA option
service_criterion = Criterion(
    name="service_rating",
    weight=8.0,
    requirement="How does the reviewer rate the service quality?",
    scale_type="ordinal",
    options=[
        CriterionOption(label="Very Poor service", value=0.0),
        CriterionOption(label="Poor service", value=0.25),
        CriterionOption(label="Average service", value=0.5),
        CriterionOption(label="Good service", value=0.75),
        CriterionOption(label="Excellent service", value=1.0),
        CriterionOption(label="N/A - Service not mentioned", value=0.0, na=True),
    ]
)

NA Handling

Options with na=True are treated like CANNOT_ASSESS for binary criteria. They're excluded from scoring when the SKIP strategy is used (default).

Step 4: Build the Complete Rubric¶

Combine multi-choice and binary criteria:

rubric = Rubric([
    # Multi-choice ordinal
    Criterion(
        name="detail_level",
        weight=10.0,
        requirement="How detailed and informative is this review?",
        scale_type="ordinal",
        options=[
            CriterionOption(label="Very Poor", value=0.0),
            CriterionOption(label="Poor", value=0.25),
            CriterionOption(label="Average", value=0.5),
            CriterionOption(label="Good", value=0.75),
            CriterionOption(label="Excellent", value=1.0),
        ]
    ),
    # Multi-choice nominal
    Criterion(
        name="review_tone",
        weight=5.0,
        requirement="What is the overall tone of this review?",
        scale_type="nominal",
        options=[
            CriterionOption(label="Positive", value=1.0),
            CriterionOption(label="Neutral", value=0.5),
            CriterionOption(label="Negative", value=0.0),
            CriterionOption(label="Mixed", value=0.5),
        ]
    ),
    # Multi-choice with NA
    Criterion(
        name="food_quality",
        weight=12.0,
        requirement="How does the reviewer rate the food quality?",
        scale_type="ordinal",
        options=[
            CriterionOption(label="Terrible", value=0.0),
            CriterionOption(label="Below Average", value=0.25),
            CriterionOption(label="Average", value=0.5),
            CriterionOption(label="Above Average", value=0.75),
            CriterionOption(label="Outstanding", value=1.0),
            CriterionOption(label="Not discussed", value=0.0, na=True),
        ]
    ),
    # Binary criterion (still supported)
    Criterion(
        name="spam_content",
        weight=-10.0,
        requirement="Review contains spam or promotional content"
    ),
])

Step 5: Configure the Grader¶

Enable option shuffling to mitigate position bias:

from autorubric import LLMConfig
from autorubric.graders import CriterionGrader

grader = CriterionGrader(
    llm_config=LLMConfig(model="openai/gpt-4.1-mini"),
    shuffle_options=True,  # Randomize option order (default)
)

Position Bias

LLMs tend to favor options presented earlier in the list. shuffle_options=True (default) randomizes presentation order and maps responses back to original indices, mitigating this bias.

Step 6: Grade and Interpret Results¶

import asyncio

review = """
Last night's dinner at Bistro Luna was exceptional. The pan-seared salmon
was perfectly cooked with a crispy skin and buttery interior. The accompanying
risotto was creamy without being heavy. Service was attentive but not intrusive -
our server knew the wine list well and made excellent pairing suggestions.
The ambiance was romantic with soft lighting and well-spaced tables.

Only minor issue: the dessert menu was limited. We opted for tiramisu which
was good but not memorable. Still, at $85 per person including wine, it's
excellent value for the quality.
"""

async def main():
    result = await rubric.grade(
        to_grade=review,
        grader=grader,
        query="Evaluate this restaurant review."
    )
    return result

result = asyncio.run(main())

# Print results
print(f"Overall Score: {result.score:.2f}\n")

for criterion in result.report:
    name = criterion.name

    if criterion.verdict is not None:
        # Binary criterion
        print(f"[{criterion.verdict.value}] {name}")
    else:
        # Multi-choice criterion
        mc = criterion.multi_choice_verdict
        print(f"[{mc.selected_label}] {name}")
        print(f"  Value: {mc.value:.2f}")
        if mc.na:
            print(f"  (Not applicable)")

    print(f"  Reason: {criterion.reason}\n")

Sample output:

Overall Score: 0.89

[Excellent] detail_level
  Value: 1.00
  Reason: Review provides specific details about dishes, service, ambiance, and pricing.

[Positive] review_tone
  Value: 1.00
  Reason: Overall positive with only minor criticism of dessert menu.

[Above Average] food_quality
  Value: 0.75
  Reason: Reviewer praises the salmon and risotto highly, mild criticism of dessert.

[UNMET] spam_content
  Reason: No promotional or spam content detected.

Step 7: Ensemble Aggregation for Multi-Choice¶

With ensembles, multi-choice votes are aggregated differently based on scale type:

grader = CriterionGrader(
    judges=[...],
    ordinal_aggregation="mean",  # Mean of values, snap to nearest option
    nominal_aggregation="mode",  # Most common selection
)

Ordinal aggregation strategies:

mean: Average values, snap to nearest option
median: Median value, snap to nearest
weighted_mean: Weighted by judge weights
mode: Most common selection

Nominal aggregation strategies:

mode: Most common selection (majority)
weighted_mode: Weighted by judge weights
unanimous: All must agree (else fallback to mode)

Key Takeaways¶

Ordinal scales for ordered options (satisfaction, quality ratings)
Nominal scales for unordered categories (sentiment, type)
Explicit values (0.0-1.0) avoid position bias in scoring
NA options handle inapplicable criteria gracefully
shuffle_options=True mitigates LLM position bias
Multi-choice and binary can coexist in the same rubric

Going Further¶

Judge Validation - Weighted kappa for ordinal agreement
Ensemble Judging - Aggregating multi-choice votes
API Reference: Multi-Choice - Full documentation

Appendix: Complete Code¶

"""Multi-Choice Rubrics - Restaurant Review Evaluation"""

import asyncio
from autorubric import Rubric, Criterion, CriterionOption, LLMConfig
from autorubric.graders import CriterionGrader


# Sample restaurant reviews
REVIEWS = [
    {
        "text": """
Last night's dinner at Bistro Luna was exceptional. The pan-seared salmon
was perfectly cooked with a crispy skin and buttery interior. The accompanying
risotto was creamy without being heavy. Service was attentive but not intrusive -
our server knew the wine list well and made excellent pairing suggestions.
The ambiance was romantic with soft lighting and well-spaced tables.

Only minor issue: the dessert menu was limited. We opted for tiramisu which
was good but not memorable. Still, at $85 per person including wine, it's
excellent value for the quality.
""",
        "description": "Detailed positive review with minor criticism"
    },
    {
        "text": """
Meh. Food was okay I guess. Nothing special. Wouldn't go out of my way to
come back but wouldn't avoid it either.
""",
        "description": "Vague neutral review"
    },
    {
        "text": """
DO NOT EAT HERE!!! Waited 45 minutes for cold pasta. The waiter was rude
when I complained. Manager didn't care. $60 wasted. ZERO STARS.
""",
        "description": "Angry negative review"
    },
    {
        "text": """
The new tasting menu is a culinary journey worth taking. Chef Maria's
interpretation of classic French techniques with local ingredients creates
unexpected harmonies. The course progression - from delicate amuse-bouche
through robust mains to ethereal desserts - demonstrates masterful pacing.

Standouts: the deconstructed bouillabaisse, and the 36-hour braised short
rib. Wine pairings ($75 supplement) are thoughtfully curated.

Note: Vegetarian tasting menu available with advance notice.
""",
        "description": "Sophisticated positive review"
    },
    {
        "text": """
Great spot for brunch! The avocado toast was Instagram-worthy and actually
tasty. Bloody Marys are strong. Gets crowded on weekends so arrive early.
Parking is tricky - use the lot behind the building.
""",
        "description": "Casual helpful review"
    },
    {
        "text": """
I had high hopes based on reviews but was disappointed. The $40 steak was
overcooked despite ordering medium-rare. However, the appetizers (especially
the crab cakes) were excellent, and the cocktails creative. Mixed bag overall.
""",
        "description": "Mixed review with specific details"
    },
    {
        "text": """
Perfect for business dinners. Private rooms available, excellent wine list,
professional service. Food is solid upscale American - nothing risky but
reliably good. Expense account friendly.
""",
        "description": "Practical business-focused review"
    },
    {
        "text": """
GET 50% OFF YOUR FIRST ORDER WITH CODE FOODIE50! This restaurant is
AMAZING and you should definitely try their new app for exclusive deals!
Download now at...
""",
        "description": "Spam/promotional content"
    }
]


async def main():
    # Build multi-choice rubric
    rubric = Rubric([
        Criterion(
            name="detail_level",
            weight=10.0,
            requirement="How detailed and informative is this review?",
            scale_type="ordinal",
            options=[
                CriterionOption(label="Very Poor - No useful details", value=0.0),
                CriterionOption(label="Poor - Minimal details", value=0.25),
                CriterionOption(label="Average - Some useful information", value=0.5),
                CriterionOption(label="Good - Detailed and helpful", value=0.75),
                CriterionOption(label="Excellent - Comprehensive with specifics", value=1.0),
            ]
        ),
        Criterion(
            name="review_tone",
            weight=5.0,
            requirement="What is the overall tone of this review?",
            scale_type="nominal",
            options=[
                CriterionOption(label="Positive", value=1.0),
                CriterionOption(label="Neutral", value=0.5),
                CriterionOption(label="Negative", value=0.0),
                CriterionOption(label="Mixed", value=0.5),
            ]
        ),
        Criterion(
            name="food_rating",
            weight=12.0,
            requirement="How does the reviewer rate the food quality?",
            scale_type="ordinal",
            options=[
                CriterionOption(label="Terrible", value=0.0),
                CriterionOption(label="Below Average", value=0.25),
                CriterionOption(label="Average", value=0.5),
                CriterionOption(label="Above Average", value=0.75),
                CriterionOption(label="Outstanding", value=1.0),
                CriterionOption(label="Not mentioned", value=0.0, na=True),
            ]
        ),
        Criterion(
            name="service_rating",
            weight=8.0,
            requirement="How does the reviewer rate the service?",
            scale_type="ordinal",
            options=[
                CriterionOption(label="Terrible", value=0.0),
                CriterionOption(label="Below Average", value=0.25),
                CriterionOption(label="Average", value=0.5),
                CriterionOption(label="Above Average", value=0.75),
                CriterionOption(label="Outstanding", value=1.0),
                CriterionOption(label="Not mentioned", value=0.0, na=True),
            ]
        ),
        Criterion(
            name="actionable_info",
            weight=6.0,
            requirement="Does the review provide actionable information (prices, tips, recommendations)?",
            scale_type="ordinal",
            options=[
                CriterionOption(label="None", value=0.0),
                CriterionOption(label="Minimal", value=0.33),
                CriterionOption(label="Moderate", value=0.67),
                CriterionOption(label="Extensive", value=1.0),
            ]
        ),
        Criterion(
            name="spam_content",
            weight=-15.0,
            requirement="Contains spam, promotional content, or fake review indicators"
        ),
    ])

    # Configure grader
    grader = CriterionGrader(
        llm_config=LLMConfig(model="openai/gpt-4.1-mini", temperature=0.0),
        shuffle_options=True,
    )

    print("=" * 70)
    print("RESTAURANT REVIEW QUALITY ASSESSMENT")
    print("=" * 70)

    for i, review in enumerate(REVIEWS, 1):
        result = await rubric.grade(
            to_grade=review["text"],
            grader=grader,
            query="Evaluate this restaurant review for quality and helpfulness."
        )

        print(f"\n{'─' * 70}")
        print(f"Review {i}: {review['description']}")
        print(f"Score: {result.score:.2f}")
        print(f"{'─' * 70}")

        for cr in result.report:
            if cr.verdict is not None:
                # Binary
                verdict_str = f"[{cr.verdict.value}]"
            else:
                # Multi-choice
                mc = cr.multi_choice_verdict
                na_marker = " (N/A)" if mc.na else ""
                verdict_str = f"[{mc.selected_label}]{na_marker}"

            print(f"  {verdict_str} {cr.name}")


if __name__ == "__main__":
    asyncio.run(main())