Repository: lm-sys/FastChat
File: fastchat/serve/monitor/monitor.py (Python)

Code before:
            "number",
            "markdown",
            "number",
            "str",
            "number",
            "str",
            "str",
            "str",
        ],
        # value=highlight_top_models(arena_vals.style),
        value=arena_vals,
        elem_id="arena_leaderboard_dataframe",
        height=1000,
        column_widths=[60, 85, 220, 90, 90, 90, 120, 150, 100],
        wrap=True,
    )

    gr.Markdown(
        f"""
***Rank (UB)**: model's ranking (upper-bound), defined by one + the number of models that are statistically better than the target model.
Model A is statistically better than model B when A's lower-bound score is greater than B's upper-bound score (in 95% confidence interval).
See Figure 1 below for visualization of the confidence intervals of model scores.

**StyleCtrl Rank Δ**: Change in model's rank if we apply Style Control. 
The resulting ranking separates model performances from pontential confounders such as answer length and markdown usage. 
See [blog post](https://blog.lmarena.ai/blog/2024/style-control/) for more details.

Note: in each category, we exclude models with fewer than 300 votes as their confidence intervals can be large.
""",
        elem_id="leaderboard_markdown",
    )

    if not vision:
        leader_component_values[:] = [default_md, p1, p2, p3, p4]

    if show_plot:
        more_stats_md = gr.Markdown(
            f"""## More Statistics for Chatbot Arena (Overall)""",
            elem_id="leaderboard_header_markdown",
        )
        with gr.Row():
            with gr.Column():
                gr.Markdown(
                    "#### Figure 1: Confidence Intervals on Model Strength (via Bootstrapping)",
                    elem_id="plot-title",
                )
                plot_3 = gr.Plot(p3, show_label=False)
            with gr.Column():
                gr.Markdown(
                    "#### Figure 2: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)",
                    elem_id="plot-title",

Review comment (performance):
```suggestion
**StyleCtrl Rank Δ**: The change in model's ranking when Style Control is applied. 
Style Control accounts for factors like response length and markdown usage to decouple model performance from these potential confounding variables.
See [blog post](https://blog.lmarena.ai/blog/2024/style-control/) for further details.
```

Code after:
        value=arena_vals,
        elem_id="arena_leaderboard_dataframe",
        height=1000,
        column_widths=[75, 75, 180, 60, 60, 60, 70, 80, 60],
        wrap=True,
    )

    gr.Markdown(
        f"""
***Rank (UB)**: model's ranking (upper-bound), defined by one + the number of models that are statistically better than the target model.
Model A is statistically better than model B when A's lower-bound score is greater than B's upper-bound score (in 95% confidence interval).
See Figure 1 below for visualization of the confidence intervals of model scores.

**Rank (StyleCtrl)**: model's ranking with style control, which accounts for factors like response length and markdown usage to decouple model performance from these potential confounding variables.
See [blog post](https://blog.lmarena.ai/blog/2024/style-control/) for further details.

Note: in each category, we exclude models with fewer than 300 votes as their confidence intervals can be large.
""",
        elem_id="leaderboard_markdown",
    )

    if not vision:
        leader_component_values[:] = [default_md, p1, p2, p3, p4]

    if show_plot:
        more_stats_md = gr.Markdown(
            f"""## More Statistics for Chatbot Arena (Overall)""",
            elem_id="leaderboard_header_markdown",
        )
        with gr.Row():
            with gr.Column():
                gr.Markdown(
                    "#### Figure 1: Confidence Intervals on Model Strength (via Bootstrapping)",
                    elem_id="plot-title",
                )
                plot_3 = gr.Plot(p3, show_label=False)
            with gr.Column():
                gr.Markdown(
                    "#### Figure 2: Average Win Rate Against All Other Models (Assuming Uniform Sampling and No Ties)",
                    elem_id="plot-title",
                )
                plot_4 = gr.Plot(p4, show_label=False)
        with gr.Row():
            with gr.Column():
                gr.Markdown(
                    "#### Figure 3: Fraction of Model A Wins for All Non-tied A vs. B Battles",
                    elem_id="plot-title",
                )
                plot_1 = gr.Plot(p1, show_label=False, elem_id="plot-container")
            with gr.Column():
                gr.Markdown(