update

app.py

@@ -105,8 +105,9 @@ def init_leaderboard(dataframe):
 # model_result_path = "./src/results/models_2024-10-08-17:39:21.001582.jsonl"
 # model_result_path = "./src/results/models_2024-10-09-05:17:38.810960.json"
 # model_result_path = "./src/results/models_2024-10-09-06:22:21.122422.json"
-model_result_path = "./src/results/models_2024-10-10-06:18:54.263527.json"
+# model_result_path = "./src/results/models_2024-10-10-06:18:54.263527.json"
 # model_result_path = "./src/results/models_2024-10-18-14:06:13.588399.json"
+model_result_path = "./src/results/models_2024-10-20-23:34:57.242641.json"
 # model_leaderboard_df = get_model_leaderboard_df(model_result_path)
 
 
@@ -156,7 +157,7 @@ with demo:
         with gr.TabItem("🏅 Overview", elem_id="llm-benchmark-tab-table", id=0):
 
             DESCRIPTION_TEXT = """
-            Total #models: 53 (Last updated: 2024-10-09)
+            Total #models: 57 (Last updated: 2024-10-21)
             
             This page prvovides a comprehensive overview of model ranks across various dimensions, based on their averaged ranks. 
             (Missing values are due to the slow or problemtic model responses to be fixed soom.)
@@ -182,7 +183,7 @@ with demo:
                 )
             )
             
-        with gr.TabItem("🎯 Overall", elem_id="llm-benchmark-tab-table", id=1):
+        with gr.TabItem("🎯 Mixed", elem_id="llm-benchmark-tab-table", id=1):
             DESCRIPTION_TEXT = """
             Overall dimension measures the comprehensive performance of LLMs across diverse tasks. 
             We start with diverse questions from the widely-used [MT-Bench](https://arxiv.org/abs/2306.05685), 
@@ -190,21 +191,23 @@ with demo:
             """
             gr.Markdown(DESCRIPTION_TEXT, elem_classes="markdown-text")
             
-            leaderboard = overall_leaderboard(
-                get_model_leaderboard_df(
-                    model_result_path,
-                    benchmark_cols=[
-                        AutoEvalColumn.rank_overall.name,
-                        AutoEvalColumn.model.name, 
-                        AutoEvalColumn.score_overall.name,
-                        AutoEvalColumn.sd_overall.name,
-                        AutoEvalColumn.license.name,
-                        AutoEvalColumn.organization.name,
-                        AutoEvalColumn.knowledge_cutoff.name,
-                        ],
-                    rank_col=[AutoEvalColumn.rank_overall.name],
-                ))
+            with gr.TabItem("MT-Bench", elem_id="mt-bench_subtab", id=0, elem_classes="subtab"): 
+                leaderboard = overall_leaderboard(
+                    get_model_leaderboard_df(
+                        model_result_path,
+                        benchmark_cols=[
+                            AutoEvalColumn.rank_overall.name,
+                            AutoEvalColumn.model.name, 
+                            AutoEvalColumn.score_overall.name,
+                            AutoEvalColumn.sd_overall.name,
+                            AutoEvalColumn.license.name,
+                            AutoEvalColumn.organization.name,
+                            AutoEvalColumn.knowledge_cutoff.name,
+                            ],
+                        rank_col=[AutoEvalColumn.rank_overall.name],
+                    ))
             
+
         with gr.TabItem("🔢 Math", elem_id="math-tab-table", id=2):
             DESCRIPTION_TEXT="""
             Algebra, Geometry, and Probability are the current three main math domains in the leaderboard. 
@@ -223,7 +226,22 @@ with demo:
             gr.Markdown(DESCRIPTION_TEXT, elem_classes="markdown-text")
 
             # leaderboard = init_leaderboard(LEADERBOARD_DF)
-            with gr.TabItem("🧮 Algebra", elem_id="algebra_subtab", id=0, elem_classes="subtab"): 
+            with gr.TabItem("Overall", elem_id="math_overall_subtab", id=0, elem_classes="subtab"): 
+                leaderboard = overall_leaderboard(
+                    get_model_leaderboard_df(
+                        model_result_path,
+                        benchmark_cols=[
+                            AutoEvalColumn.model.name, 
+                            AutoEvalColumn.rank_math_algebra.name,
+                            AutoEvalColumn.rank_math_geometry.name,
+                            AutoEvalColumn.rank_math_probability.name,
+                            ],
+                        rank_col=[],
+                    )
+                )
+
+
+            with gr.TabItem("🧮 Algebra", elem_id="algebra_subtab", id=1, elem_classes="subtab"): 
                 leaderboard = overall_leaderboard(
                     get_model_leaderboard_df(
                         model_result_path,
@@ -231,7 +249,7 @@ with demo:
                             AutoEvalColumn.rank_math_algebra.name,
                             AutoEvalColumn.model.name, 
                             AutoEvalColumn.score_math_algebra.name,
-                            AutoEvalColumn.sd_math_algebra.name,
+                            # AutoEvalColumn.sd_math_algebra.name,
                             AutoEvalColumn.license.name,
                             AutoEvalColumn.organization.name,
                             AutoEvalColumn.knowledge_cutoff.name,
@@ -240,7 +258,7 @@ with demo:
                     )
                 )
                 
-            with gr.TabItem("📐 Geometry", elem_id="geometry_subtab", id=1, elem_classes="subtab"): 
+            with gr.TabItem("📐 Geometry", elem_id="geometry_subtab", id=2, elem_classes="subtab"): 
                 leaderboard = overall_leaderboard(
                     get_model_leaderboard_df(
                         model_result_path,
@@ -248,7 +266,7 @@ with demo:
                             AutoEvalColumn.rank_math_geometry.name,
                             AutoEvalColumn.model.name, 
                             AutoEvalColumn.score_math_geometry.name,
-                            AutoEvalColumn.sd_math_geometry.name,
+                            # AutoEvalColumn.sd_math_geometry.name,
                             AutoEvalColumn.license.name,
                             AutoEvalColumn.organization.name,
                             AutoEvalColumn.knowledge_cutoff.name,
@@ -257,7 +275,7 @@ with demo:
                     )
                 )
 
-            with gr.TabItem("📊 Probability", elem_id="prob_subtab", id=2, elem_classes="subtab"):
+            with gr.TabItem("📊 Probability", elem_id="prob_subtab", id=3, elem_classes="subtab"):
                 leaderboard = overall_leaderboard(
                     get_model_leaderboard_df(
                         model_result_path,
@@ -265,7 +283,7 @@ with demo:
                             AutoEvalColumn.rank_math_probability.name,
                             AutoEvalColumn.model.name, 
                             AutoEvalColumn.score_math_probability.name,
-                            AutoEvalColumn.sd_math_probability.name,
+                            # AutoEvalColumn.sd_math_probability.name,
                             AutoEvalColumn.license.name,
                             AutoEvalColumn.organization.name,
                             AutoEvalColumn.knowledge_cutoff.name,
@@ -299,7 +317,20 @@ with demo:
             """
             gr.Markdown(DESCRIPTION_TEXT, elem_classes="markdown-text")
 
-            with gr.TabItem("🧩 Logical", elem_id="logical_subtab", id=0, elem_classes="subtab"):         
+            with gr.TabItem("Overall", elem_id="reasoning_overall_subtab", id=0, elem_classes="subtab"): 
+                leaderboard = overall_leaderboard(
+                    get_model_leaderboard_df(
+                        model_result_path,
+                        benchmark_cols=[
+                            AutoEvalColumn.model.name, 
+                            AutoEvalColumn.rank_reason_logical.name,
+                            AutoEvalColumn.rank_reason_social.name,
+                            ],
+                        rank_col=[],
+                    )
+                )
+
+            with gr.TabItem("🧩 Logical", elem_id="logical_subtab", id=1, elem_classes="subtab"):         
                 leaderboard = overall_leaderboard(
                     get_model_leaderboard_df(
                         model_result_path,
@@ -307,7 +338,7 @@ with demo:
                             AutoEvalColumn.rank_reason_logical.name,
                             AutoEvalColumn.model.name, 
                             AutoEvalColumn.score_reason_logical.name,
-                            AutoEvalColumn.sd_reason_logical.name,
+                            # AutoEvalColumn.sd_reason_logical.name,
                             AutoEvalColumn.license.name,
                             AutoEvalColumn.organization.name,
                             AutoEvalColumn.knowledge_cutoff.name,
@@ -316,7 +347,7 @@ with demo:
                     )
                 )
 
-            with gr.TabItem("🗣️ Social", elem_id="social_subtab", id=1, elem_classes="subtab"):         
+            with gr.TabItem("🗣️ Social", elem_id="social_subtab", id=2, elem_classes="subtab"):         
                 leaderboard = overall_leaderboard(
                     get_model_leaderboard_df(
                         model_result_path,
@@ -324,7 +355,7 @@ with demo:
                             AutoEvalColumn.rank_reason_social.name,
                             AutoEvalColumn.model.name, 
                             AutoEvalColumn.score_reason_social.name,
-                            AutoEvalColumn.sd_reason_social.name,
+                            # AutoEvalColumn.sd_reason_social.name,
                             AutoEvalColumn.license.name,
                             AutoEvalColumn.organization.name,
                             AutoEvalColumn.knowledge_cutoff.name,
@@ -348,7 +379,19 @@ with demo:
             """
             gr.Markdown(CURRENT_TEXT, elem_classes="markdown-text")
             
-            with gr.TabItem("🧪 Chemistry", elem_id="chemistry_subtab", id=0, elem_classes="subtab"):         
+            with gr.TabItem("Overall", elem_id="science_overall_subtab", id=0, elem_classes="subtab"): 
+                leaderboard = overall_leaderboard(
+                    get_model_leaderboard_df(
+                        model_result_path,
+                        benchmark_cols=[
+                            AutoEvalColumn.model.name, 
+                            AutoEvalColumn.rank_chemistry.name,
+                            ],
+                        rank_col=[],
+                    )
+                )
+
+            with gr.TabItem("🧪 Chemistry", elem_id="chemistry_subtab", id=1, elem_classes="subtab"):         
                 leaderboard = overall_leaderboard(
                     get_model_leaderboard_df(
                         model_result_path,

src/display/utils.py

@@ -64,35 +64,48 @@ auto_eval_column_dict.append(["score_sd", ColumnContent, field(default_factory=l
 auto_eval_column_dict.append(["rank", ColumnContent, field(default_factory=lambda: ColumnContent("Rank", "number", True))])
 
 # fine-grained dimensions
-auto_eval_column_dict.append(["score_overall", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Overall)", "number", True))])
-auto_eval_column_dict.append(["score_math_algebra", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Math Algebra)", "number", True))])
-auto_eval_column_dict.append(["score_math_geometry", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Math Geometry)", "number", True))])
-auto_eval_column_dict.append(["score_math_probability", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Math Probability)", "number", True))])
-auto_eval_column_dict.append(["score_reason_logical", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Logical Reasoning)", "number", True))])
-auto_eval_column_dict.append(["score_reason_social", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Social Reasoning)", "number", True))])
+auto_eval_column_dict.append(["score_overall", ColumnContent, field(default_factory=lambda: ColumnContent("Score (MT-Bench)", "number", True))])
+auto_eval_column_dict.append(["sd_overall", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev(MT-Bench)", "number", True))])
+auto_eval_column_dict.append(["rank_overall", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (MT-Bench)", "number", True))])
 
-auto_eval_column_dict.append(["sd_overall", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev(Overall)", "number", True))])
+auto_eval_column_dict.append(["score_math_algebra", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Math Algebra)", "number", True))])
 auto_eval_column_dict.append(["sd_math_algebra", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Math Algebra)", "number", True))])
-auto_eval_column_dict.append(["sd_math_geometry", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Math Geometry)", "number", True))])
-auto_eval_column_dict.append(["sd_math_probability", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Math Probability)", "number", True))])
-auto_eval_column_dict.append(["sd_reason_logical", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Logical Reasoning)", "number", True))])
-auto_eval_column_dict.append(["sd_reason_social", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Social Reasoning)", "number", True))])
-
-auto_eval_column_dict.append(["rank_overall", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Overall)", "number", True))])
 auto_eval_column_dict.append(["rank_math_algebra", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Math Algebra)", "number", True))])
+
+auto_eval_column_dict.append(["score_math_geometry", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Math Geometry)", "number", True))])
+auto_eval_column_dict.append(["sd_math_geometry", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Math Geometry)", "number", True))])
 auto_eval_column_dict.append(["rank_math_geometry", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Math Geometry)", "number", True))])
+
+auto_eval_column_dict.append(["score_math_probability", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Math Probability)", "number", True))])
+auto_eval_column_dict.append(["sd_math_probability", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Math Probability)", "number", True))])
 auto_eval_column_dict.append(["rank_math_probability", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Math Probability)", "number", True))])
+
+auto_eval_column_dict.append(["score_reason_logical", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Logical Reasoning)", "number", True))])
+auto_eval_column_dict.append(["sd_reason_logical", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Logical Reasoning)", "number", True))])
 auto_eval_column_dict.append(["rank_reason_logical", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Logical Reasoning)", "number", True))])
+
+auto_eval_column_dict.append(["score_reason_social", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Social Reasoning)", "number", True))])
+auto_eval_column_dict.append(["sd_reason_social", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Social Reasoning)", "number", True))])
 auto_eval_column_dict.append(["rank_reason_social", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Social Reasoning)", "number", True))])
                              
 auto_eval_column_dict.append(["score_chemistry", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Chemistry)", "number", True))])
 auto_eval_column_dict.append(["sd_chemistry", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Chemistry)", "number", True))])
 auto_eval_column_dict.append(["rank_chemistry", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Chemistry)", "number", True))])
 
+auto_eval_column_dict.append(["score_physics", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Physics)", "number", True))])
+auto_eval_column_dict.append(["sd_physics", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Physics)", "number", True))])
+auto_eval_column_dict.append(["rank_physics", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Physics)", "number", True))])
+
+auto_eval_column_dict.append(["score_biology", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Biology)", "number", True))])
+auto_eval_column_dict.append(["sd_biology", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Biology)", "number", True))])
+auto_eval_column_dict.append(["rank_biology", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Biology)", "number", True))])
+
+
 auto_eval_column_dict.append(["score_cpp", ColumnContent, field(default_factory=lambda: ColumnContent("Score (C++)", "number", True))])
 auto_eval_column_dict.append(["sd_cpp", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (C++)", "number", True))])
 auto_eval_column_dict.append(["rank_cpp", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (C++)", "number", True))])
 
+
 for task in Tasks:
     auto_eval_column_dict.append([task.name, ColumnContent, field(default_factory=lambda: ColumnContent(task.value.col_name, "number", True))])
 auto_eval_column_dict.append(["model_type_symbol", ColumnContent, field(default_factory=lambda: ColumnContent("T", "str", True, never_hidden=True))])

src/populate.py

@@ -15,14 +15,20 @@ def get_model_leaderboard_df(results_path: str, requests_path: str="", cols: lis
     """Creates a dataframe from all the individual experiment results"""
     raw_data = get_raw_model_results(results_path)
     all_data_json = [v.to_dict() for v in raw_data]
+    assert len(rank_col) <= 1, "Only one column can be selected for ranking"
 
     df = pd.DataFrame.from_records(all_data_json)
     
     df = df[benchmark_cols]
     # print(df.head())
     
-    if rank_col: # if there is one col in rank_col, sort by that column and remove NaN values
-        df = df.dropna(subset=benchmark_cols)
+    # if there is one col in rank_col, this is an isolated dimension to rank by
+    # sort by that selected column and remove NaN values
+    if rank_col: 
+        # df = df.dropna(subset=benchmark_cols)
+        df = df.dropna(subset=rank_col)
+        df = df.fillna(0.00)
+        # print(df[rank_col[0]])
         df = df.sort_values(by=[rank_col[0]], ascending=True)
         # print(rank_col, benchmark_cols)
         # print(df.head())
@@ -31,7 +37,7 @@ def get_model_leaderboard_df(results_path: str, requests_path: str="", cols: lis
         avg_rank = df.iloc[:, 1:].mean(axis=1)
         df["Average Rank"] = avg_rank.round(decimals=4)
         df = df.sort_values(by=["Average Rank"], ascending=True)        
-        df["Average Rank"] = df["Average Rank"].map('{:.4f}'.format)
+        df["Average Rank"] = df["Average Rank"].map('{:.2f}'.format)
 
         # we'll skip NaN, instrad of deleting the whole row
         df = df.fillna('--')
@@ -41,19 +47,25 @@ def get_model_leaderboard_df(results_path: str, requests_path: str="", cols: lis
         
     
     for col in benchmark_cols:
-        # print(col)
-        # if 'Std dev' in col or 'Score' in col:
         if 'Std dev' in col or 'Score' in col:
-        #     if set(['Chemistry', 'Reasoning']).intersection(set(col.split())): 
-        #         df[col] = (df[col]).map('{:.2f}'.format)
-        #     else:   
-        #         df[col] = (df[col]*100).map('{:.2f}'.format)
-            if "Chemistry" in col or "C++" in col:
-            # if "Chemistry" in col or "C++" in col or "Overall" in col or "Probability" in col or "Logical" in col:
-                df[col] = (df[col]).map('{:.2f}'.format)
-            else:
-                df[col] = (df[col]*100).map('{:.2f}'.format)
+            df[col] = (df[col]).map('{:.2f}'.format)
             df[col] = df[col].round(decimals=2)
+                        
+            
+    # for col in benchmark_cols:
+    #     # print(col)
+    #     # if 'Std dev' in col or 'Score' in col:
+    #     if 'Std dev' in col or 'Score' in col:
+    #     #     if set(['Chemistry', 'Reasoning']).intersection(set(col.split())): 
+    #     #         df[col] = (df[col]).map('{:.2f}'.format)
+    #     #     else:   
+    #     #         df[col] = (df[col]*100).map('{:.2f}'.format)
+    #         # if "Chemistry" in col or "C++" in col:
+    #         if "Chemistry" in col or "C++" in col or "Overall" in col or "Probability" in col or "Logical" in col:
+    #             df[col] = (df[col]).map('{:.2f}'.format)
+    #         else:
+    #             df[col] = (df[col]*100).map('{:.2f}'.format)
+    #         df[col] = df[col].round(decimals=2)
 
     # df = df.sort_values(by=[AutoEvalColumn.score.name], ascending=True)
     # df[AutoEvalColumn.rank.name] = df[AutoEvalColumn.score.name].rank(ascending=True, method="min")

src/results/models_2024-10-20-23:34:57.242641.json

@@ -0,0 +1,2802 @@
+[
+    {
+        "config": {
+            "model_name": "ChatGPT-4o-latest (2024-09-03)",
+            "organization": "OpenAI",
+            "license": "Proprietary",
+            "knowledge_cutoff": "2023/10"
+        },
+        "results": {
+            "OVERALL": {
+                "Average Score": 93.51557945652831,
+                "Standard Deviation": 3.1900396436407785,
+                "Rank": 4
+            },
+            "Geometry": {
+                "Average Score": 81.8536937387725,
+                "Standard Deviation": null,
+                "Rank": 5
+            },
+            "Algebra": {
+                "Average Score": 89.3642910524324,
+                "Standard Deviation": null,
+                "Rank": 3
+            },
+            "Probability": {
+                "Average Score": 86.55761073510537,
+                "Standard Deviation": null,
+                "Rank": 4
+            },
+            "Logical": {
+                "Average Score": 97.39734315785844,
+                "Standard Deviation": null,
+                "Rank": 2
+            },
+            "Social": {
+                "Average Score": 91.03727530739368,
+                "Standard Deviation": null,
+                "Rank": 7
+            },
+            "Chemistry": {
+                "Average Score": 100.0,
+                "Standard Deviation": null,
+                "Rank": 1
+            },
+            "CPP": {
+                "Average Score": 100.0,
+                "Standard Deviation": null,
+                "Rank": 1
+            }
+        }
+    },
+    {
+        "config": {
+            "model_name": "gpt-4o-2024-08-06",
+            "organization": "OpenAI",
+            "license": "Proprietary",
+            "knowledge_cutoff": "2023/10"
+        },
+        "results": {
+            "OVERALL": {
+                "Average Score": 79.7806321863411,
+                "Standard Deviation": 0.8302330946013555,
+                "Rank": 14
+            },
+            "Geometry": {
+                "Average Score": 86.29041459755453,
+                "Standard Deviation": null,
+                "Rank": 2
+            },
+            "Algebra": {
+                "Average Score": 88.53373721863113,
+                "Standard Deviation": null,
+                "Rank": 4
+            },
+            "Probability": {
+                "Average Score": 78.694360721361,
+                "Standard Deviation": null,
+                "Rank": 7
+            },
+            "Logical": {
+                "Average Score": 78.3116623496895,
+                "Standard Deviation": null,
+                "Rank": 12
+            },
+            "Social": {
+                "Average Score": 79.90944696263446,
+                "Standard Deviation": null,
+                "Rank": 11
+            },
+            "Chemistry": {
+                "Average Score": 86.96011263543132,
+                "Standard Deviation": null,
+                "Rank": 7
+            },
+            "CPP": {
+                "Average Score": 92.43090226400756,
+                "Standard Deviation": null,
+                "Rank": 2
+            }
+        }
+    },
+    {
+        "config": {
+            "model_name": "gpt-4o-2024-05-13",
+            "organization": "OpenAI",
+            "license": "Proprietary",
+            "knowledge_cutoff": "2023/10"
+        },
+        "results": {
+            "OVERALL": {
+                "Average Score": 86.40675398236253,
+                "Standard Deviation": 6.473604235710212,
+                "Rank": 9
+            },
+            "Geometry": {
+                "Average Score": 82.42032988843268,
+                "Standard Deviation": null,
+                "Rank": 4
+            },
+            "Algebra": {
+                "Average Score": 83.51580675782952,
+                "Standard Deviation": null,
+                "Rank": 9
+            },
+            "Probability": {
+                "Average Score": 81.88434691830915,
+                "Standard Deviation": null,
+                "Rank": 5
+            },
+            "Logical": {
+                "Average Score": 87.92744931984977,
+                "Standard Deviation": null,
+                "Rank": 9
+            },
+            "Social": {
+                "Average Score": 76.12369632852445,
+                "Standard Deviation": null,
+                "Rank": 15
+            },
+            "Chemistry": {
+                "Average Score": 90.93459148149344,
+                "Standard Deviation": null,
+                "Rank": 4
+            },
+            "CPP": {
+                "Average Score": 79.1592634699295,
+                "Standard Deviation": null,
+                "Rank": 6
+            }
+        }
+    },
+    {
+        "config": {
+            "model_name": "gpt-4-turbo-2024-04-09",
+            "organization": "OpenAI",
+            "license": "Proprietary",
+            "knowledge_cutoff": "2023/12"
+        },
+        "results": {
+            "OVERALL": {
+                "Average Score": 87.17581147282237,
+                "Standard Deviation": 8.716963621850567,
+                "Rank": 8
+            },
+            "Geometry": {
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