diff --git a/analysis/plot_results.py b/analysis/plot_results.py
index 2f97f9f..2fbb64f 100644
--- a/analysis/plot_results.py
+++ b/analysis/plot_results.py
@@ -170,7 +170,7 @@ def plot_eng_drop_line(
     for (label, group), color in zip(data.groupby("Model_Type"), colors):
         mrewardbench_scores = group["Avg_Multilingual"]
         rewardbench_scores = group["eng_Latn"]
-        ax.scatter(rewardbench_scores, mrewardbench_scores, marker="o", s=30, label=label, color=color)
+        ax.scatter(rewardbench_scores, mrewardbench_scores, marker="o", s=40, label=label, color=color)
 
     mrewardbench_scores = data["Avg_Multilingual"]
     rewardbench_scores = data["eng_Latn"]
@@ -184,6 +184,7 @@ def plot_eng_drop_line(
     ax.plot([min_val, max_val], [min_val, max_val], linestyle="--", color="black", alpha=0.25)
     ax.set_xlabel("RewardBench (Lambert et al., 2024)")
     ax.set_ylabel("M-RewardBench")
+    ax.grid(color="gray", alpha=0.2, which="both")
     ax.set_aspect("equal")
     ax.legend(frameon=False, handletextpad=0.2, fontsize=12)
 
@@ -193,7 +194,7 @@ def plot_eng_drop_line(
             rewardbench_scores[idx],
             mrewardbench_scores[idx],
             model_names[idx],
-            fontsize=12,
+            fontsize=14,
         )
         for idx in range(len(data))
     ]
@@ -216,8 +217,8 @@ def plot_eng_drop_line(
     #     # bbox=dict(facecolor="white", edgecolor="black", boxstyle="round,pad=0.5"),
     # )
 
-    ax.spines["right"].set_visible(False)
-    ax.spines["top"].set_visible(False)
+    # ax.spines["right"].set_visible(False)
+    # ax.spines["top"].set_visible(False)
     plt.tight_layout()
     fig.savefig(output_path, bbox_inches="tight")
 
@@ -238,7 +239,7 @@ def plot_ling_dims(
     raw = pd.read_csv(input_path).set_index("Model")
     if top_n:
         raw = raw.head(top_n)
-    raw = raw[[col for col in raw.columns if col not in ("Model_Type", "eng_Latn", "Avg_Multilingual")]]
+    raw = raw[[col for col in raw.columns if col not in ("Model_Type", "eng_Latn", "Avg_Multilingual", "Family")]]
     raw = raw.T
     langdata = pd.read_csv(langdata).set_index("Language")
     combined = raw.merge(langdata, left_index=True, right_index=True)
@@ -246,6 +247,9 @@ def plot_ling_dims(
     combined["Std"] = raw.std(axis=1) * 100
 
     combined = combined.rename(columns={"Resource_Type": "Resource Availability"})
+    # Remove Class 0 because it's misleading
+    combined = combined[combined["Resource Availability"] != "Class-0"].reset_index()
+
     linguistic_dims = [
         "Resource Availability",
         "Family",
@@ -254,22 +258,28 @@ def plot_ling_dims(
     fig, axs = plt.subplots(1, len(linguistic_dims), figsize=figsize, sharex=True)
     for ax, dim in zip(axs, linguistic_dims):
         lingdf = combined.groupby(dim).agg({"Avg": "mean", "Std": "mean"}).reset_index()
+        if dim != "Resource Availability":
+            lingdf = lingdf.sort_values(by="Avg", ascending=False)
+        else:
+            lingdf = lingdf[::-1]
 
+        ax.grid(color="gray", alpha=0.2, which="both", axis="x")
+        ax.set_axisbelow(True)
         sns.barplot(
             x="Avg",
             y=dim,
             data=lingdf,
             ax=ax,
             color="green",
-            width=0.5 if dim == "Resource Availability" else 0.7,
+            width=0.4 if dim == "Resource Availability" else 0.7,
         )
         ax.set_title(dim)
         ax.set_xlim([60, 70])
         ax.set_ylabel("")
         ax.set_xlabel("M-RewardBench Score")
 
-        ax.spines["right"].set_visible(False)
-        ax.spines["top"].set_visible(False)
+        # ax.spines["right"].set_visible(False)
+        # ax.spines["top"].set_visible(False)
 
     plt.tight_layout()
     fig.savefig(output_path, bbox_inches="tight")