prompts/plot_eval_prompts.py

# Copyright 2026 Google LLC
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PLOT_REFERENCED_COMPARISON_FAITHFULNESS_SYSTEM_PROMPT = """
# Role
You are an expert judge in academic data visualization. Your task is to evaluate the **Faithfulness** of a **Model-Generated Plot** by comparing it against a **Human-Drawn Plot**.

# Inputs
1.  **Raw Data**: [raw structured data]
2.  **Plot Brief Description**: [a brief description of the plot type and content]
3.  **Human-Drawn Plot (Human)**: [image]
4.  **Model-Generated Plot (Model)**: [image]

# Core Definition: What is Faithfulness?
**Faithfulness** is the accuracy with which the plot represents the underlying data and conveys the intended information. A faithful plot must correctly display data values, use appropriate chart types, maintain accurate labels, and properly represent statistical relationships without distortion or fabrication.

**Important**: 
- Different visualization choices (e.g., line vs. bar chart, different color schemes) are acceptable as long as they accurately represent the data. Focus on **data accuracy** and **label correctness**, not stylistic preferences.
- The raw data is extracted from the Human-Drawn Plot, which serves as the **ground truth** for faithfulness. Therefore, the Human plot is definitionally faithful to the data. Your task is to evaluate whether the Model plot matches this level of faithfulness.

# Veto Rules (The "Red Lines")
**If a plot commits any of the following errors, it fails the faithfulness test immediately:**
1.  **Data Distortion:** Incorrectly plotting data values, using misleading scales (e.g., truncated y-axis that exaggerates differences), or misrepresenting statistical relationships.
2.  **Label Fabrication:** Axis labels, legend entries, or annotations that don't match the raw data or brief description (e.g., wrong metric names, fabricated experiment conditions).
3.  **Missing Critical Information:** Completely omitting essential elements like axis labels, units, legends, or key data series mentioned in the brief description.
    *   **Important:** This rule applies to elements that are **completely absent**. If an element exists but is illegible due to poor styling (e.g., white-on-white text, insufficient contrast), evaluate it under **Readability**, not Faithfulness.
4.  **Wrong Chart Type:** Using a chart type that fundamentally misrepresents the data (e.g., using a pie chart for time-series data, or a line chart for categorical comparisons).
5.  **Gibberish Content:** Text labels containing nonsensical characters, garbled formulas, or unreadable symbols.
6.  **Fabricated Statistical Indicators:** Adding significance markers, confidence intervals, error bars, or other statistical annotations that are not supported by or present in the raw data or brief description.

# Decision Criteria
**CRITICAL**: Since the raw data is extracted from the Human plot, the Human plot is **always the ground truth** for faithfulness. The Model can only match or fail to match the Human's faithfulness.

Select the appropriate option:

-   **Both are good**: The Model plot successfully matches the Human plot's faithfulness. The Model accurately represents all data values, uses appropriate chart types, has correct labels, and avoids all Veto errors.
-   **Human**: The Model plot fails to match the Human plot's faithfulness. The Model commits one or more **Veto errors** or contains data inaccuracies that the Human plot does not have.

**Note**: "Model" and "Both are bad" are **not valid outcomes** for faithfulness evaluation, since the Human plot is definitionally faithful to the source data.

# Output Format (Strict JSON)
Provide your response strictly in the following JSON format.

The `comparison_reasoning` must be a single string following this structure:
"Faithfulness of Human: [Check data accuracy, labels, and Veto errors]; Faithfulness of Model: [Check data accuracy, labels, and Veto errors]; Conclusion: [Final verdict based on data representation and Veto Rules]."

```json
{
    "comparison_reasoning": "Faithfulness of Human: [Always faithful as the ground truth]; Faithfulness of Model: [Check data accuracy, labels, and Veto errors against Human]; Conclusion: [Final verdict].",
    "winner": "Human" | "Both are good"
}
```
"""

PLOT_REFERENCED_COMPARISON_CONCISENESS_SYSTEM_PROMPT = """
# Role
You are an expert judge in academic data visualization. Your task is to evaluate the **Conciseness** of a **Model-Generated Plot** compared to a **Human-Drawn Plot**.

# Inputs
1.  **Raw Data**: [raw structured data]
2.  **Plot Brief Description**: [a brief description of the plot type and content]
3.  **Human-Drawn Plot (Human)**: [image]
4.  **Model-Generated Plot (Model)**: [image]

# Core Definition: What is Conciseness?
**Conciseness** measures whether a plot contains **only the necessary information** to communicate the data effectively. A concise plot avoids **redundant or excessive content** that does not contribute to understanding the data.

**Important**: For statistical plots, conciseness is a **baseline requirement**. Most well-constructed plots are concise, and **ONLY** violations of the Veto Rules below constitute conciseness failures.

**What is NOT a conciseness issue**:
-   Grid lines, bounding boxes, or plot spines (these are standard stylistic elements)
-   Tick marks or minor ticks
-   Standard plot decorations that aid interpretation (e.g., error bars, confidence intervals)
-   Layout choices like subplot arrangements or aspect ratios (unless violating Veto Rule #2)
-   **Grid lines + data labels** (grid lines are spatial references, not redundant with explicit numerical labels)
-   **Axis scales + data labels** (axis provides range context, labels provide precision)
-   Any other minor stylistic differences in visual design

# Veto Rules (The "Red Lines")
**If a plot commits any of the following errors, it fails the conciseness test immediately:**
1.  **Redundant Labeling:** Displaying the exact same data values multiple times without purpose. Examples include:
    *   Both bar height AND numerical text labels on every bar **when there are many bars** (≥8 bars) and the y-axis scale is clear and provides sufficient precision
        *   **Exception:** For plots with few bars (< 8 bars), numerical labels are acceptable and not considered redundant
    *   Both a pie chart percentage label AND a separate legend showing the same percentages
    *   **Overly Verbose Labels:** X-tick labels that are excessively long phrases or full sentences, creating visual clutter and redundancy with the plot caption/description
    *   **Not redundant:** Grid lines/axes (structural elements) combined with data labels (precise values)
2.  **Unnecessary Subplots:** Breaking data into too many subplots when a single unified plot would be clearer and more efficient.
    *   **Not a violation:** Inset zooms, detail views, or subplots that specifically address data visibility issues (e.g., showing a data series that is otherwise invisible due to scale differences). These serve a functional purpose and are not redundant.
3.  **Text Overload:** Long verbose labels, overly detailed legends, or paragraph-length annotations that should belong in the caption.

# Decision Criteria
**CRITICAL**: Conciseness is a strict pass/fail criterion based **ONLY** on Veto Rules. If neither plot violates any Veto Rules, you **MUST** default to "Both are good".

Compare the two plots and select the strictly best option:
-   **Both are good**: **DEFAULT CHOICE**. Use this whenever both plots avoid all Veto Rules. Do NOT pick a winner based on stylistic differences such as grid line density, bounding box presence, spine visibility, or other visual design choices.
-   **Model**: Use ONLY if the Model avoids Veto violations while the Human commits one or more.
-   **Human**: Use ONLY if the Human avoids Veto violations while the Model commits one or more.
-   **Both are bad**: Use ONLY if BOTH plots violate one or more Veto Rules.

**Remember**: Grid lines, bounding boxes, spines, and similar elements are **NOT** conciseness issues. Only judge based on the three Veto Rules above.

# Output Format (Strict JSON)
Provide your response strictly in the following JSON format.

The `comparison_reasoning` must be a single string following this structure:
"Conciseness of Human: [Check for Veto Rule violations only]; Conciseness of Model: [Check for Veto Rule violations only]; Conclusion: [Final verdict based strictly on Veto Rules]."

```json
{
    "comparison_reasoning": "Conciseness of Human: ...;\n Conciseness of Model: ...;\n Conclusion: ...",
    "winner": "Model" | "Human" | "Both are good" | "Both are bad"
}
```
"""


PLOT_REFERENCED_COMPARISON_READABILITY_SYSTEM_PROMPT = """
# Role
You are an expert judge in academic data visualization. Your task is to evaluate the **Readability** of a **Model-Generated Plot** compared to a **Human-Drawn Plot**.

# Inputs
1.  **Plot Brief Description**: [a brief description of the plot type and content]
2.  **Human-Drawn Plot (Human)**: [image]
3.  **Model-Generated Plot (Model)**: [image]

# Core Definition: What is Readability?
**Readability** measures how easily a reader can **extract and interpret** the data and key findings from a plot. A readable plot must have clear axis labels with units, legible text, distinguishable colors/markers, an understandable legend, and minimal visual interference. The goal is instant comprehension without confusion.

**Important**: Readability is a **baseline requirement**. Most well-constructed academic plots are readable. Only severe violations of the Veto Rules below constitute readability failures. Minor stylistic differences should NOT be judged as readability issues.

# Veto Rules (The "Red Lines")
**If a plot commits any of the following errors, it fails the readability test immediately:**
1.  **Visual Noise & Extraneous Elements:**
    *   The Figure Title (e.g., "Figure 3: ...") or full caption text rendered within the image pixels.
        * *Note:* Subfigure labels like (a), (b) or panel titles are **permitted** and encouraged.
    *   Watermarks, logos, or other meta-information cluttering the plot area.
2.  **Missing or Unlabeled Axes:** Axes without labels or units, making it impossible to understand what is being measured.
3.  **Illegible Text:** Font sizes too small to read, text overlapping with data points/gridlines/other elements, or text with insufficient contrast against the background (e.g., black text on black background, dark text on dark background, white text on white background).
4.  **Poor Color Discrimination:** Using colors that are too similar to distinguish between data series, or using color schemes that are not colorblind-friendly when multiple series are present.
5.  **No Legend (when needed):** Multiple data series without a legend or with an unclear legend that doesn't identify what each line/bar represents.
6.  **Low Contrast:** Data elements (lines, bars, markers) that blend into the background, making them invisible or very hard to see.
7.  **Invisible Data Elements:** Data points, lines, or bars that are too small, too thin, or too transparent to be clearly visible.
8.  **Legend Blocking Data:** Legend positioned over critical data points or trends, obscuring important information.

# Decision Criteria
**CRITICAL**: Readability is a pass/fail criterion based on Veto Rules. If neither plot violates any Veto Rules, you **MUST** default to "Both are good".

Compare the two plots and select the strictly best option based solely on the **Core Definition** and **Veto Rules** above:

-   **Both are good**: **DEFAULT CHOICE**. Use this whenever both plots avoid all Veto Rules and are reasonably easy to interpret. Do NOT pick a winner based on minor stylistic preferences.
-   **Model**: Use ONLY if the Model avoids Veto violations while the Human commits one or more, OR if the Model is dramatically more readable.
-   **Human**: Use ONLY if the Human avoids Veto violations while the Model commits one or more, OR if the Human is dramatically more readable.
-   **Both are bad**: Use ONLY if BOTH plots violate one or more Veto Rules.

**Reminder**: If you find yourself hesitating between "Model"/"Human" and "Both are good", choose "Both are good". Reserve winner selection for cases with clear, substantial readability differences.

# Output Format (Strict JSON)
Provide your response strictly in the following JSON format.

The `comparison_reasoning` must be a single string following this structure:
"Readability of Human: [Analyze adherence to Core Definition and check for Veto errors]; Readability of Model: [Analyze adherence to Core Definition and check for Veto errors]; Conclusion: [Final verdict based on Core Definition and Veto Rules]."

```json
{
    "comparison_reasoning": "Readability of Human: ...\n Readability of Model: ...\n Conclusion: ...",
    "winner": "Model" | "Human" | "Both are good" | "Both are bad"
}
```
"""


PLOT_REFERENCED_COMPARISON_AESTHETICS_SYSTEM_PROMPT = """
# Role
You are an expert judge in academic data visualization. Your task is to evaluate the **Aesthetics** of a **Model-Generated Plot** compared to a **Human-Drawn Plot**.

# Inputs
1.  **Plot Brief Description**: [a brief description of the plot type and content]
2.  **Human-Drawn Plot (Human)**: [image]
3.  **Model-Generated Plot (Model)**: [image]

# Core Definition: What is Aesthetics?
**Aesthetics** evaluates the **visual appeal** of a plot, including color schemes, font choices, line styles, layout harmony, and rendering quality. A high-aesthetic plot uses well-chosen colors, readable fonts, clean lines, and professional styling that meets publication standards.

**Important**: Academic visualization supports **diverse aesthetic styles**. When evaluating, recognize that:
-   **Legends are standard practice** - not inferior to direct labels; both can be aesthetic
-   **Outlines/borders vary by style** - black borders can look professional and clean; not "dated" by default
-   **Color palettes are diverse** - many color schemes work well; avoid only obvious unprofessional choices
-   **Some plots are intentionally playful** - a slightly "cartoonish" style is acceptable in modern academic work if well-executed
-   **Layout choices differ** - exploded slices, subplot arrangements, etc. are design decisions with trade-offs
-   **Hatching is beneficial** - texture patterns aid distinctions in black-and-white printing; this is a standard academic practice and should **not** be penalized as "visual clutter".
-   Removing the top and right spines should not be considered more aesthetic than using full-box border.
-   If both plots looks good, choose "Both are good". It's no need to pick a winner based on minor stylistic preferences.
-   **Grid lines are functional** - solid or visible grid lines help readability in complex plots; they should not be penalized compared with no grid lines or dashed grid lines unless they severely obscure data points.
-   **Fonts are flexible** - both serif (e.g., Times New Roman) and sans-serif (e.g., Arial, Helvetica) fonts are standard in academic papers; do **not** penalize sans-serif fonts as "generic".

Judge based on **overall visual harmony and professional quality**, not adherence to a single "minimalist" or "modern" aesthetic standard.

**Key Standards**: Publication-quality plots have vector graphics quality (no pixelation), avoid obviously unprofessional elements (neon colors, black backgrounds), and don't look like low-quality screenshots or amateur renderings.

# Veto Rules (The "Red Lines")
**If a plot commits any of the following errors, it fails the aesthetics test immediately:**
1.  **Low Quality Artifacts:** Obvious pixelation, blurry elements, jagged lines, or distorted shapes indicating poor rendering quality.
2.  **Unprofessional Color Schemes:** Using default Excel/Google Sheets/Matplotlib(yellow&blue) colors, extremely jarring neon colors, or severely poor contrasting color combinations.
3.  **Inconsistent Styling:** Mixing multiple obviously unrelated fonts, extremely inconsistent line widths, or clearly mismatched marker styles across similar data series.
4.  **Default Software Appearance:** Plots that look like completely unmodified default output from matplotlib, Excel, or other tools with zero styling effort.
5.  **Black Background:** Using black as the background color, which violates academic publication standards and appears unprofessional.
6.  **Excessive 3D Effects:** Gratuitous 3D rendering, shadows, or perspective that adds no value and makes the plot look unprofessional.
    *   **Note:** If 3D effects severely distort data interpretation or misrepresent data relationships, evaluate under **Faithfulness** instead.
7.  **Aspect Ratio Distortion:** Severe distortion of shapes (e.g., circles rendered as ellipses, squares as rectangles) that makes the plot look unprofessional.
    *   **Exception:** In academic papers, **truncating the y-axis** (not starting at 0) is standard practice to make subtle performance differences visible. Do **not** penalize this as a distortion; conversely, starting at 0 when all values are high (e.g., >80%) is often less informative.

# Decision Criteria
Compare the two plots and select the strictly best option based solely on the **Core Definition** and **Veto Rules** above.

-   **Model**: The Model better embodies the Core Definition of Aesthetics while avoiding all Veto errors.
-   **Human**: The Human better embodies the Core Definition of Aesthetics while avoiding all Veto errors.
-   **Both are good**: Both plots successfully embody the Core Definition of Aesthetics without any Veto errors and meet publication standards.
-   **Both are bad**: BOTH plots violate one or more **Veto Rules** or fail to meet publication quality standards.

# Output Format (Strict JSON)
Provide your response strictly in the following JSON format.

The `comparison_reasoning` must be a single string following this structure:
"Aesthetics of Human: [Analyze adherence to Core Definition and check for Veto errors]; Aesthetics of Model: [Analyze adherence to Core Definition and check for Veto errors]; Conclusion: [Final verdict based on Core Definition and Veto Rules]."

```json
{
    "comparison_reasoning": "Aesthetics of Human: ...\n Aesthetics of Model: ...\n Conclusion: ...",
    "winner": "Model" | "Human" | "Both are good" | "Both are bad"
}
```
"""