remove winrates and update the length penalty method

app.py

@@ -32,7 +32,9 @@ with open("_about_us.md", "r") as f:
 with open("_header.md", "r") as f:
     HEADER_MD = f.read()
 
+LP_MODE = "v2"
 original_df, ablation_df = None, None
+LP_original_dfs = {} 
 eval_results = load_eval_results() 
  
 available_models = [] # to be filled in later
@@ -68,31 +70,33 @@ def display_chat_history(model_selections, task_selections):
 
 
 def slider_change_main(length_penalty):
-    global original_df, ablation_df
-    adjusted_df = apply_length_penalty(original_df, ablation_df, length_penalty) 
+    global original_df, ablation_df, LP_MODE
+    adjusted_df = apply_length_penalty(original_df, ablation_df, length_penalty, mode=LP_MODE, LP_original_dfs=LP_original_dfs) 
     adjusted_df = adjusted_df[["Model", "Overall Elo", "Task-Avg Elo", "# battles", "Length"]]
     adjusted_df = adjusted_df.sort_values(by="Overall Elo", ascending=False)
-    adjusted_df = add_winrates(adjusted_df) 
-    adjusted_df = adjusted_df.drop(columns=["Length"])
+    # adjusted_df = add_winrates(adjusted_df, LP=length_penalty) 
+    # adjusted_df = adjusted_df.drop(columns=["Length"])
     return adjusted_df
 
 def slider_change_full(length_penalty, show_winrate):
-    global original_df, ablation_df
-    adjusted_df = apply_length_penalty(original_df, ablation_df, length_penalty)
+    global original_df, ablation_df, LP_MODE
+    adjusted_df = apply_length_penalty(original_df, ablation_df, length_penalty, mode=LP_MODE, LP_original_dfs=LP_original_dfs)
     # sort the model by the "Task-Avg Elo" column
-    adjusted_df = adjusted_df.sort_values(by="Task-Avg Elo", ascending=False)
+    adjusted_df = adjusted_df.sort_values(by="Overall Elo", ascending=False)
     adjusted_df.drop(columns=["Overall Elo", "Task-Avg Elo", "# battles", "Length"], inplace=True)
     if show_winrate == "none":
         return adjusted_df
     elif show_winrate == "gpt-3.5":
-        adjusted_df = add_winrates_tasks(adjusted_df, ref="gpt-3.5")
+        adjusted_df = add_winrates_tasks(adjusted_df, ref="gpt-3.5", LP=length_penalty)
     elif show_winrate == "gpt-4":
-        adjusted_df = add_winrates_tasks(adjusted_df, ref="gpt-4")
+        adjusted_df = add_winrates_tasks(adjusted_df, ref="gpt-4", LP=length_penalty)
     return adjusted_df
 
 seafoam = Seafoam()
 def build_demo(TYPES):
     global original_df, ablation_df, skip_empty_original_df, skip_empty_ablation_df, available_models
+    global LP_original_dfs, LP_MODE
+
     with gr.Blocks(theme=gr.themes.Soft(), css=css, js=js_light) as demo:
     # with gr.Blocks(theme=seafoam, css=css) as demo:
         gr.HTML(BANNER, elem_id="banner")
@@ -106,14 +110,16 @@ def build_demo(TYPES):
                 
                 with gr.TabItem("Main Table", elem_id="od-benchmark-tab-table-ablation", id=0, elem_classes="subtab"):
                     # original_df, ablation_df = skip_empty_original_df, skip_empty_ablation_df
-                    default_main_df = apply_length_penalty(original_df, ablation_df, length_penalty=DEFAULT_LP) 
+                    default_main_df = apply_length_penalty(original_df, ablation_df, length_penalty=DEFAULT_LP, mode=LP_MODE, LP_original_dfs=LP_original_dfs) 
                     default_main_df = default_main_df[["Model", "Overall Elo", "Task-Avg Elo", "# battles", "Length"]] 
-                    default_main_df = add_winrates(default_main_df)
-                    default_main_df = default_main_df.drop(columns=["Length"])
+                    default_main_df = default_main_df.sort_values(by="Overall Elo", ascending=False)
+                    # default_main_df = add_winrates(default_main_df, LP=DEFAULT_LP)
+                    # default_main_df = default_main_df.drop(columns=["Overall Elo"])
+                    # default_main_df = default_main_df.drop(columns=["Length"])
                     # TODO: add the win rate for GPT-4 and GPT-3.5T
                     with gr.Row():
                         with gr.Column(scale=4):
-                            gr.Markdown("**Overall Elo**: [Standard Elo rating with boostrap.](https://en.wikipedia.org/wiki/Elo_rating_system). | **Task-Avg Elo**: Compute Elo on subsets of each task type and then take avg. | **Win Rates**: [Estimated by Elo differences](https://www.hexwiki.net/index.php/Elo_rating#Definition). | **Length penalty**: Models w/ longer outputs are penalized. (Plz check 📖 **Details**.)", elem_classes="markdown-text-small top-left-LP") 
+                            gr.Markdown("**Task-Avg Elo**: Compute Elo on subsets of each task type and then take their avg. | **Win Rates**: [Estimated by Elo differences](https://www.hexwiki.net/index.php/Elo_rating#Definition). | **Length penalty**: Models w/ longer outputs are penalized. (Plz check 📖 **Details**.)", elem_classes="markdown-text-small top-left-LP") 
                         with gr.Column(scale=0.8):
                             length_penlty_slider = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=DEFAULT_LP, label="Length Penalty", elem_id="length-penalty-slider") 
                     # checkbox_skip_empty = gr.Checkbox(label="Skip empty results", value=False, elem_id="skip-empty-checkbox", scale=2)
@@ -130,16 +136,17 @@ def build_demo(TYPES):
                     length_penlty_slider.change(fn=slider_change_main, inputs=[length_penlty_slider], outputs=[leaderboard_table])
                 
 
-                with gr.TabItem("All Tasks (Win% vs GPT-3.5T)", elem_id="od-benchmark-tab-table-ablation", id=1):
+                with gr.TabItem("All Tasks (Win% vs GPT-3.5T)", elem_id="od-benchmark-tab-table-ablation", id=1, visible=False):
                     with gr.Row():
                         with gr.Column(scale=4):
                             gr.Markdown(TASK_TYPE_STR, elem_classes="markdown-text-small top-left-LP") 
                         with gr.Column(scale=0.8):
                             length_penlty_slider_full = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=DEFAULT_LP, label="Length Penalty", elem_id="length-penalty-slider")
-                    default_full_df = apply_length_penalty(original_df, ablation_df, length_penalty=DEFAULT_LP)
+                    default_full_df = apply_length_penalty(original_df, ablation_df, length_penalty=DEFAULT_LP, mode=LP_MODE, LP_original_dfs=LP_original_dfs)
                     # do not show the "# battles" column here 
+                    default_full_df = default_full_df.sort_values(by="Overall Elo", ascending=False)
                     default_full_df = default_full_df.drop(columns=["Overall Elo", "Task-Avg Elo", "# battles", "Length"])
-                    default_full_df = add_winrates_tasks(default_full_df, ref="gpt-3.5")
+                    default_full_df = add_winrates_tasks(default_full_df, ref="gpt-3.5", LP=DEFAULT_LP)                    
 
                     leaderboard_table_full = gr.components.Dataframe(
                         value=default_full_df,
@@ -155,16 +162,18 @@ def build_demo(TYPES):
                     length_penlty_slider_full.change(fn=slider_change_full, inputs=[length_penlty_slider_full, show_winrate], outputs=[leaderboard_table_full])
 
 
-                with gr.TabItem("All Tasks (Win% vs GPT-4)", elem_id="od-benchmark-tab-table-ablation", id=2):
+                with gr.TabItem("All Tasks (Win% vs GPT-4)", elem_id="od-benchmark-tab-table-ablation", id=2, visible=False):
                     with gr.Row():
                         with gr.Column(scale=4):
                             gr.Markdown(TASK_TYPE_STR, elem_classes="markdown-text-small top-left-LP") 
                         with gr.Column(scale=0.8):
                             length_penlty_slider_full = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=DEFAULT_LP, label="Length Penalty", elem_id="length-penalty-slider")
-                    default_full_df = apply_length_penalty(original_df, ablation_df, length_penalty=DEFAULT_LP)
+                    default_full_df = apply_length_penalty(original_df, ablation_df, length_penalty=DEFAULT_LP, mode=LP_MODE, LP_original_dfs=LP_original_dfs)
                     # do not show the "# battles" column here 
+                    default_full_df = default_full_df.sort_values(by="Overall Elo", ascending=False)
                     default_full_df = default_full_df.drop(columns=["Overall Elo", "Task-Avg Elo", "# battles", "Length"])
-                    default_full_df = add_winrates_tasks(default_full_df, ref="gpt-4")
+                    default_full_df = add_winrates_tasks(default_full_df, ref="gpt-4", LP=DEFAULT_LP)
+                    
                     leaderboard_table_full = gr.components.Dataframe(
                         value=default_full_df,
                         datatype=TYPES,
@@ -185,7 +194,7 @@ def build_demo(TYPES):
                             gr.Markdown(TASK_TYPE_STR, elem_classes="markdown-text-small top-left-LP") 
                         with gr.Column(scale=0.8):
                             length_penlty_slider_full = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=DEFAULT_LP, label="Length Penalty", elem_id="length-penalty-slider")
-                    default_full_df = apply_length_penalty(original_df, ablation_df, length_penalty=DEFAULT_LP)
+                    default_full_df = apply_length_penalty(original_df, ablation_df, length_penalty=DEFAULT_LP, mode=LP_MODE, LP_original_dfs=LP_original_dfs)
                     # do not show the "# battles" column here 
                     default_full_df = default_full_df.drop(columns=["Overall Elo", "Task-Avg Elo", "# battles", "Length"])
                     leaderboard_table_full = gr.components.Dataframe(
@@ -427,6 +436,7 @@ if __name__ == "__main__":
     parser.add_argument("--length_balation_file", help="Path to results table", default="data_dir/elo_ranks.length_ablation.all.jsonl")
     parser.add_argument("--skip_empty_result_file", help="Path to results table", default="data_dir/elo_ranks.skip_empty.all.jsonl")
     parser.add_argument("--skip_empty_length_balation_file", help="Path to results table", default="data_dir/elo_ranks.skip_empty.length_ablation.all.jsonl")
+    
     args = parser.parse_args()
 
     LAST_UPDATED = datetime.fromtimestamp(Path(args.result_file).stat().st_mtime, tz=timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
@@ -435,17 +445,32 @@ if __name__ == "__main__":
     ablation_df = pd.read_json(args.length_balation_file, lines=True)
     skip_empty_original_df = pd.read_json(args.skip_empty_result_file , lines=True)
     skip_empty_ablation_df = pd.read_json(args.skip_empty_length_balation_file, lines=True)
+
+    
+    for i in range(0, 11):
+        if i == 0:
+            L = "0.0"
+        elif 1 <= i <= 9:
+            L = f"0.{i}"
+        elif i == 10:
+            L = "1.0" 
+        result_file_path = args.result_file.replace(".jsonl", f".L={L}.jsonl")
+        LP_original_dfs[L] = pd.read_json(result_file_path, lines=True)
+    
+    
    
     
     # available_models = sorted(list(set(list(original_df["model name "])))) 
     available_models = list(model_info.keys())
+    model_len_info = json.load(open("model_len_info.json", "r"))
+
     # remove the rows where the model name is not in the available_models
     original_df = original_df[original_df["model name "].isin(available_models)]
     ablation_df = ablation_df[ablation_df["model name "].isin(available_models)]
     skip_empty_ablation_df = skip_empty_ablation_df[skip_empty_ablation_df["model name "].isin(available_models)]
     skip_empty_original_df = skip_empty_original_df[skip_empty_original_df["model name "].isin(available_models)]
 
-    model_len_info = json.load(open("model_len_info.json", "r"))
+
 
     original_df = post_processing(original_df, model_len_info)
     ablation_df = post_processing(ablation_df, model_len_info)
@@ -453,7 +478,11 @@ if __name__ == "__main__":
     skip_empty_ablation_df = post_processing(skip_empty_ablation_df, model_len_info)
     
 
-    
+    for LP, LP_origin_df in LP_original_dfs.items():
+        LP_original_dfs[LP] = LP_origin_df[LP_origin_df["model name "].isin(available_models)]
+        LP_original_dfs[LP] = post_processing(LP_original_dfs[LP], model_len_info)
+
+
 
     TYPES = ["markdown", "number"]
 

compute_model_output_len.py

@@ -8,6 +8,7 @@ length_info = {}
 for model_name in tqdm(list(model_info.keys())): 
     result = load_infer_results(model_name)
     lens = []
+    cnt_empty = 0
     for item in result:
         o = item["output"]
         if type(o) == list:
@@ -16,9 +17,11 @@ for model_name in tqdm(list(model_info.keys())):
             L = len(o.strip())
         if L > 0:
             lens.append(L)
+        else:
+            cnt_empty += 1
     avg_len = sum(lens) / len(lens)
-    print(f"{model_name}: {avg_len}")
-    length_info[model_name] = avg_len
+    print(f"{model_name}: {avg_len}; {cnt_empty} empty outputs.")
+    length_info[model_name] = {"avg_len": avg_len, "empty_output": cnt_empty, "num_samples": len(result)}
 
 with open("model_len_info.json", "w") as f:
     json.dump(length_info, f, indent=2)

constants.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 
-DEFAULT_LP = 0.3
+DEFAULT_LP = 0.5
 
 banner_url = "https://allenai.github.io/WildBench/gray_banner.png" # the same repo here.
 BANNER = f'<div style="display: flex; justify-content: flex-start;"><img src="{banner_url}" alt="Banner" style="width: 40vw; min-width: 300px; max-width: 800px;"> </div>'

data_dir/elo_ranks.L=0.0.jsonl

@@ -0,0 +1,24 @@
+{"model name ": "gpt-4-0125-preview", "elo overall": 1293, "# battles": 5781}
+{"model name ": "claude-3-opus-20240229", "elo overall": 1153, "# battles": 3658}
+{"model name ": "claude-3-sonnet-20240229", "elo overall": 1134, "# battles": 2791}
+{"model name ": "mistral-large-2402", "elo overall": 1117, "# battles": 2058}
+{"model name ": "Nous-Hermes-2-Mixtral-8x7B-DPO", "elo overall": 1099, "# battles": 1484}
+{"model name ": "Mistral-7B-Instruct-v0.2", "elo overall": 1099, "# battles": 2519}
+{"model name ": "Mixtral-8x7B-Instruct-v0.1", "elo overall": 1068, "# battles": 3619}
+{"model name ": "gemini-1.0-pro", "elo overall": 1067, "# battles": 2035}
+{"model name ": "Yi-34B-Chat", "elo overall": 1053, "# battles": 2606}
+{"model name ": "Llama-2-70b-chat-hf.nosp", "elo overall": 1027, "# battles": 2144}
+{"model name ": "zephyr-7b-gemma-v0.1", "elo overall": 1022, "# battles": 1532}
+{"model name ": "Llama-2-13b-chat-hf.nosp", "elo overall": 1004, "# battles": 2091}
+{"model name ": "tulu-2-dpo-70b", "elo overall": 990, "# battles": 3630}
+{"model name ": "Llama-2-7b-chat-hf.nosp", "elo overall": 981, "# battles": 2094}
+{"model name ": "zephyr-7b-beta", "elo overall": 977, "# battles": 3543}
+{"model name ": "gpt-3.5-turbo-0125", "elo overall": 973, "# battles": 14196}
+{"model name ": "gemma-7b-it", "elo overall": 947, "# battles": 2728}
+{"model name ": "command", "elo overall": 941, "# battles": 1939}
+{"model name ": "Llama-2-70b-chat-hf", "elo overall": 884, "# battles": 2461}
+{"model name ": "gemma-2b-it", "elo overall": 859, "# battles": 2689}
+{"model name ": "Llama-2-13b-chat-hf", "elo overall": 853, "# battles": 2406}
+{"model name ": "vicuna-13b-v1.5", "elo overall": 843, "# battles": 2715}
+{"model name ": "Mistral-7B-Instruct-v0.1", "elo overall": 819, "# battles": 2659}
+{"model name ": "Llama-2-7b-chat-hf", "elo overall": 804, "# battles": 2366}

data_dir/elo_ranks.L=0.1.jsonl

@@ -0,0 +1,24 @@
+{"model name ": "gpt-4-0125-preview", "elo overall": 1207, "# battles": 5781}
+{"model name ": "claude-3-opus-20240229", "elo overall": 1130, "# battles": 3658}
+{"model name ": "claude-3-sonnet-20240229", "elo overall": 1112, "# battles": 2791}
+{"model name ": "mistral-large-2402", "elo overall": 1088, "# battles": 2058}
+{"model name ": "gemini-1.0-pro", "elo overall": 1070, "# battles": 2035}
+{"model name ": "Yi-34B-Chat", "elo overall": 1068, "# battles": 2606}
+{"model name ": "Mistral-7B-Instruct-v0.2", "elo overall": 1056, "# battles": 2519}
+{"model name ": "Nous-Hermes-2-Mixtral-8x7B-DPO", "elo overall": 1051, "# battles": 1484}
+{"model name ": "Mixtral-8x7B-Instruct-v0.1", "elo overall": 1047, "# battles": 3619}
+{"model name ": "Llama-2-70b-chat-hf.nosp", "elo overall": 1025, "# battles": 2144}
+{"model name ": "gpt-3.5-turbo-0125", "elo overall": 1014, "# battles": 14196}
+{"model name ": "Llama-2-13b-chat-hf.nosp", "elo overall": 1006, "# battles": 2091}
+{"model name ": "zephyr-7b-gemma-v0.1", "elo overall": 990, "# battles": 1532}
+{"model name ": "Llama-2-7b-chat-hf.nosp", "elo overall": 984, "# battles": 2094}
+{"model name ": "tulu-2-dpo-70b", "elo overall": 966, "# battles": 3630}
+{"model name ": "gemma-7b-it", "elo overall": 954, "# battles": 2728}
+{"model name ": "zephyr-7b-beta", "elo overall": 950, "# battles": 3543}
+{"model name ": "command", "elo overall": 938, "# battles": 1939}
+{"model name ": "Llama-2-70b-chat-hf", "elo overall": 931, "# battles": 2461}
+{"model name ": "Llama-2-13b-chat-hf", "elo overall": 909, "# battles": 2406}
+{"model name ": "vicuna-13b-v1.5", "elo overall": 908, "# battles": 2715}
+{"model name ": "gemma-2b-it", "elo overall": 905, "# battles": 2689}
+{"model name ": "Llama-2-7b-chat-hf", "elo overall": 865, "# battles": 2366}
+{"model name ": "Mistral-7B-Instruct-v0.1", "elo overall": 831, "# battles": 2659}

data_dir/elo_ranks.L=0.2.jsonl

@@ -0,0 +1,24 @@
+{"model name ": "gpt-4-0125-preview", "elo overall": 1170, "# battles": 5781}
+{"model name ": "claude-3-opus-20240229", "elo overall": 1126, "# battles": 3658}
+{"model name ": "claude-3-sonnet-20240229", "elo overall": 1109, "# battles": 2791}
+{"model name ": "mistral-large-2402", "elo overall": 1089, "# battles": 2058}
+{"model name ": "gemini-1.0-pro", "elo overall": 1071, "# battles": 2035}
+{"model name ": "Yi-34B-Chat", "elo overall": 1057, "# battles": 2606}
+{"model name ": "Mixtral-8x7B-Instruct-v0.1", "elo overall": 1044, "# battles": 3619}
+{"model name ": "Mistral-7B-Instruct-v0.2", "elo overall": 1044, "# battles": 2519}
+{"model name ": "Nous-Hermes-2-Mixtral-8x7B-DPO", "elo overall": 1041, "# battles": 1484}
+{"model name ": "gpt-3.5-turbo-0125", "elo overall": 1038, "# battles": 14196}
+{"model name ": "Llama-2-70b-chat-hf.nosp", "elo overall": 1009, "# battles": 2144}
+{"model name ": "Llama-2-13b-chat-hf.nosp", "elo overall": 991, "# battles": 2091}
+{"model name ": "zephyr-7b-gemma-v0.1", "elo overall": 984, "# battles": 1532}
+{"model name ": "Llama-2-7b-chat-hf.nosp", "elo overall": 969, "# battles": 2094}
+{"model name ": "gemma-7b-it", "elo overall": 968, "# battles": 2728}
+{"model name ": "tulu-2-dpo-70b", "elo overall": 965, "# battles": 3630}
+{"model name ": "zephyr-7b-beta", "elo overall": 948, "# battles": 3543}
+{"model name ": "command", "elo overall": 944, "# battles": 1939}
+{"model name ": "Llama-2-70b-chat-hf", "elo overall": 938, "# battles": 2461}
+{"model name ": "vicuna-13b-v1.5", "elo overall": 925, "# battles": 2715}
+{"model name ": "gemma-2b-it", "elo overall": 925, "# battles": 2689}
+{"model name ": "Llama-2-13b-chat-hf", "elo overall": 920, "# battles": 2406}
+{"model name ": "Llama-2-7b-chat-hf", "elo overall": 880, "# battles": 2366}
+{"model name ": "Mistral-7B-Instruct-v0.1", "elo overall": 849, "# battles": 2659}

data_dir/elo_ranks.L=0.3.jsonl

@@ -0,0 +1,24 @@
+{"model name ": "gpt-4-0125-preview", "elo overall": 1145, "# battles": 5781}
+{"model name ": "claude-3-opus-20240229", "elo overall": 1122, "# battles": 3658}
+{"model name ": "claude-3-sonnet-20240229", "elo overall": 1107, "# battles": 2791}
+{"model name ": "mistral-large-2402", "elo overall": 1092, "# battles": 2058}
+{"model name ": "gemini-1.0-pro", "elo overall": 1074, "# battles": 2035}
+{"model name ": "gpt-3.5-turbo-0125", "elo overall": 1053, "# battles": 14196}
+{"model name ": "Yi-34B-Chat", "elo overall": 1048, "# battles": 2606}
+{"model name ": "Mixtral-8x7B-Instruct-v0.1", "elo overall": 1042, "# battles": 3619}
+{"model name ": "Mistral-7B-Instruct-v0.2", "elo overall": 1037, "# battles": 2519}
+{"model name ": "Nous-Hermes-2-Mixtral-8x7B-DPO", "elo overall": 1034, "# battles": 1484}
+{"model name ": "Llama-2-70b-chat-hf.nosp", "elo overall": 997, "# battles": 2144}
+{"model name ": "zephyr-7b-gemma-v0.1", "elo overall": 983, "# battles": 1532}
+{"model name ": "Llama-2-13b-chat-hf.nosp", "elo overall": 980, "# battles": 2091}
+{"model name ": "gemma-7b-it", "elo overall": 977, "# battles": 2728}
+{"model name ": "tulu-2-dpo-70b", "elo overall": 966, "# battles": 3630}
+{"model name ": "Llama-2-7b-chat-hf.nosp", "elo overall": 957, "# battles": 2094}
+{"model name ": "command", "elo overall": 946, "# battles": 1939}
+{"model name ": "zephyr-7b-beta", "elo overall": 944, "# battles": 3543}
+{"model name ": "Llama-2-70b-chat-hf", "elo overall": 942, "# battles": 2461}
+{"model name ": "vicuna-13b-v1.5", "elo overall": 938, "# battles": 2715}
+{"model name ": "gemma-2b-it", "elo overall": 937, "# battles": 2689}
+{"model name ": "Llama-2-13b-chat-hf", "elo overall": 926, "# battles": 2406}
+{"model name ": "Llama-2-7b-chat-hf", "elo overall": 889, "# battles": 2366}
+{"model name ": "Mistral-7B-Instruct-v0.1", "elo overall": 863, "# battles": 2659}

data_dir/elo_ranks.L=0.4.jsonl

@@ -0,0 +1,24 @@
+{"model name ": "gpt-4-0125-preview", "elo overall": 1127, "# battles": 5781}
+{"model name ": "claude-3-opus-20240229", "elo overall": 1119, "# battles": 3658}
+{"model name ": "claude-3-sonnet-20240229", "elo overall": 1106, "# battles": 2791}
+{"model name ": "mistral-large-2402", "elo overall": 1095, "# battles": 2058}
+{"model name ": "gemini-1.0-pro", "elo overall": 1079, "# battles": 2035}
+{"model name ": "gpt-3.5-turbo-0125", "elo overall": 1064, "# battles": 14196}
+{"model name ": "Mixtral-8x7B-Instruct-v0.1", "elo overall": 1041, "# battles": 3619}
+{"model name ": "Yi-34B-Chat", "elo overall": 1040, "# battles": 2606}
+{"model name ": "Mistral-7B-Instruct-v0.2", "elo overall": 1032, "# battles": 2519}
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data_dir/elo_ranks.L=0.5.jsonl

@@ -0,0 +1,24 @@
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data_dir/elo_ranks.L=0.6.jsonl

@@ -0,0 +1,24 @@
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data_dir/elo_ranks.L=0.7.jsonl

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data_dir/elo_ranks.L=0.8.jsonl

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data_dir/elo_ranks.L=0.9.jsonl

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data_dir/elo_ranks.L=1.0.jsonl

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data_dir/elo_ranks.all.L=0.0.jsonl

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data_dir/elo_ranks.all.L=0.1.jsonl

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data_dir/elo_ranks.all.L=0.2.jsonl

@@ -0,0 +1,24 @@
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data_dir/elo_ranks.all.L=0.3.jsonl

@@ -0,0 +1,24 @@
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data_dir/elo_ranks.all.L=0.4.jsonl

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data_dir/elo_ranks.all.L=0.5.jsonl

@@ -0,0 +1,24 @@
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data_dir/elo_ranks.all.L=0.6.jsonl

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data_dir/elo_ranks.all.L=0.7.jsonl

@@ -0,0 +1,24 @@
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data_dir/elo_ranks.all.L=0.8.jsonl

@@ -0,0 +1,24 @@
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data_dir/elo_ranks.all.L=0.9.jsonl

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data_dir/elo_ranks.all.L=1.0.jsonl

@@ -0,0 +1,24 @@
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data_dir/elo_ranks.all.jsonl

@@ -1,22 +1,24 @@
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data_dir/elo_ranks.jsonl

@@ -1,22 +1,24 @@
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data_dir/elo_ranks.length_ablation.all.jsonl

@@ -1,22 +1,24 @@
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data_dir/elo_ranks.length_ablation.jsonl

@@ -1,22 +1,24 @@
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data_dir/elo_ranks.skip_empty.all.jsonl

@@ -1,22 +1,24 @@
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data_dir/elo_ranks.skip_empty.jsonl

@@ -1,22 +1,24 @@
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-{"model name ": "Mistral-7B-Instruct-v0.2", "elo overall": 1089, "# battles": 2313}
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-{"model name ": "Llama-2-13b-chat-hf.nosp", "elo overall": 1035, "# battles": 1657}
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data_dir/elo_ranks.skip_empty.length_ablation.all.jsonl

@@ -1,22 +1,24 @@
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data_dir/elo_ranks.skip_empty.length_ablation.jsonl

@@ -1,22 +1,24 @@
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-{"model name ": "Llama-2-13b-chat-hf.nosp", "elo overall": 1148, "# battles": 1657}
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-{"model name ": "claude-3-sonnet-20240229", "elo overall": 994, "# battles": 2483}
-{"model name ": "Mixtral-8x7B-Instruct-v0.1", "elo overall": 991, "# battles": 3350}
-{"model name ": "claude-3-opus-20240229", "elo overall": 986, "# battles": 2259}
-{"model name ": "Llama-2-70b-chat-hf", "elo overall": 985, "# battles": 2090}
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-{"model name ": "Llama-2-13b-chat-hf", "elo overall": 966, "# battles": 2045}
-{"model name ": "mistral-large-2402", "elo overall": 958, "# battles": 1794}
-{"model name ": "gemini-1.0-pro", "elo overall": 951, "# battles": 1644}
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-{"model name ": "gemma-7b-it", "elo overall": 889, "# battles": 2430}
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-{"model name ": "gemma-2b-it", "elo overall": 854, "# battles": 2410}
-{"model name ": "vicuna-13b-v1.5", "elo overall": 854, "# battles": 2170}
-{"model name ": "gpt-3.5-turbo-0125", "elo overall": 789, "# battles": 13333}
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+{"model name ": "vicuna-13b-v1.5", "elo overall": 876, "# battles": 2381}
+{"model name ": "gemma-2b-it", "elo overall": 870, "# battles": 2613}
+{"model name ": "gpt-3.5-turbo-0125", "elo overall": 812, "# battles": 13526}

data_dir/elo_ranks.test.jsonl

@@ -0,0 +1,23 @@
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+{"model name ": "Llama-2-13b-chat-hf.nosp", "elo overall": 959, "# battles": 2064}
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+{"model name ": "Llama-2-7b-chat-hf.nosp", "elo overall": 938, "# battles": 2055}
+{"model name ": "zephyr-7b-beta", "elo overall": 936, "# battles": 3511}
+{"model name ": "vicuna-13b-v1.5", "elo overall": 936, "# battles": 2666}
+{"model name ": "Llama-2-70b-chat-hf", "elo overall": 930, "# battles": 2571}
+{"model name ": "Llama-2-13b-chat-hf", "elo overall": 917, "# battles": 2507}
+{"model name ": "Llama-2-7b-chat-hf", "elo overall": 879, "# battles": 2479}
+{"model name ": "Mistral-7B-Instruct-v0.1", "elo overall": 870, "# battles": 2658}

data_dir/pairwise_win_fractions.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:53b8c101b7473e39e483771317b1bd34c9feda733d4b5cdc67b3508b2ca968b1
-size 11827
+oid sha256:75a83042bb54bc81d4bb63cebfe75fe04c1ca11f715783379750165a0968f5ca
+size 12668

data_utils.py

@@ -6,11 +6,12 @@ from constants import column_names, all_task_types
 from utils_display import make_clickable_model
 import random 
 disable_progress_bar()
+import math 
 
 id_to_data = None 
 model_len_info = None 
 
-def estimated_win_rate(elo_a, elo_b):
+def estimated_win_rate(elo_a, elo_b, LP=0):
     """
     Calculate the estimated win rate for player A against player B using their Elo ratings.
 
@@ -18,7 +19,7 @@ def estimated_win_rate(elo_a, elo_b):
     :param elo_b: Elo rating of player B
     :return: Estimated win rate for player A
     """
-    exponent = (elo_b - elo_a) / 400
+    exponent = (elo_b - elo_a)*(10**LP) / 400
     probability_a_wins = 1 / (1 + 10 ** exponent)
     return (1-probability_a_wins)*100
 
@@ -33,9 +34,9 @@ def formatter(x):
     return x
 
 
-def add_winrates(current_df):
+def add_winrates(current_df, LP=0):
     df = current_df.copy()
-    elo_column = "Overall Elo" 
+    elo_column = "Task-Avg Elo" 
 
     # Correct way to filter the DataFrame and get the Elo rating for "gpt-4-0125-preview"
     model_a_elo = df[df["Model"].str.contains("gpt-4")][elo_column].iloc[0]
@@ -45,8 +46,8 @@ def add_winrates(current_df):
 
     
     # Calculate the win rate of "gpt-4-0125-preview" against all models
-    df['Win% vs GPT-4'] = df[elo_column].apply(lambda x: estimated_win_rate(model_a_elo, x)).apply(formatter)    
-    df['Win% vs GPT-3.5T'] = df[elo_column].apply(lambda x: estimated_win_rate(model_b_elo, x)).apply(formatter)    
+    df['Win% vs GPT-4'] = df[elo_column].apply(lambda x: estimated_win_rate(model_a_elo, x, LP=LP)).apply(formatter)    
+    df['Win% vs GPT-3.5T'] = df[elo_column].apply(lambda x: estimated_win_rate(model_b_elo, x, LP=LP)).apply(formatter)    
     # apply the formatter for the two new columns 
     cols = list(df.columns)
     cols.remove("# battles"); cols.append("# battles")
@@ -54,18 +55,18 @@ def add_winrates(current_df):
     df = df[cols]
     return df
 
-def add_winrates_tasks(current_df, ref="gpt-4"):
+def add_winrates_tasks(current_df, ref="gpt-4", LP=0):
     new_df = current_df.copy()
     for t in all_task_types:
         column = column_names[t]
         model_a_elo = current_df[current_df["Model"].str.contains(ref)][column].iloc[0]
-        new_df[column] = current_df[column].apply(lambda x: estimated_win_rate(model_a_elo, x)).apply(formatter)
+        new_df[column] = current_df[column].apply(lambda x: estimated_win_rate(model_a_elo, x, LP=LP)).apply(formatter)
     return new_df
         
 
 def post_processing(df, model_len_info):
     if model_len_info:
-        df["Length"] = df["model name "].apply(lambda x: model_len_info[x])
+        df["Length"] = df["model name "].apply(lambda x: model_len_info[x]["avg_len"])
 
     for col in df.columns:
         if col == "model name ":
@@ -73,13 +74,16 @@ def post_processing(df, model_len_info):
         else:
             df[col] = df[col].apply(formatter) # For numerical values 
     df.rename(columns=column_names, inplace=True)
-    df.sort_values(by="Overall Elo", inplace=True, ascending=False)
+    df.sort_values(by="Task-Avg Elo", inplace=True, ascending=False)
     # put the "Overall Elo" and "Task-Avg Elo" column to the front
     # add the length info
-    df = df[["Model", "Overall Elo", "Task-Avg Elo"] + [col for col in df.columns if col not in ["Model", "Overall Elo", "Task-Avg Elo"]]]
+    df = df[["Model", "Task-Avg Elo"] + [col for col in df.columns if col not in ["Model", "Task-Avg Elo"]]]
     return df
 
-def apply_length_penalty(original_df, ablation_df, length_penalty=0.2):
+def apply_length_penalty(original_df, ablation_df, length_penalty=0.2, mode='v1', LP_original_dfs=None):
+    if mode == 'v2' and LP_original_dfs is not None:
+        L = f"{length_penalty:.1f}"
+        return LP_original_dfs[L]
     original_df = original_df.copy()
     ablation_df = ablation_df.copy()
     # replace all values in original_df with the values as z = x - y * length_penalty where y is from ablation_df at the same row and column
@@ -92,11 +96,14 @@ def apply_length_penalty(original_df, ablation_df, length_penalty=0.2):
             # assert that the model names are the same in both dataframes
             assert original_df.at[i, "Model"] == ablation_df[ablation_df["Model"] == row["Model"]]["Model"].values[0]
             original_df[col] = original_df[col].astype(float)
-            original_df.at[i, col] = original_df.at[i, col] - ablation_df[ablation_df["Model"] == row["Model"]][col].values[0] * length_penalty
+            if mode == "v1":
+                original_df.at[i, col] = original_df.at[i, col] - ablation_df[ablation_df["Model"] == row["Model"]][col].values[0] * length_penalty
+            elif mode == "v1.1":
+                diff = original_df.at[i, col] - ablation_df[ablation_df["Model"] == row["Model"]][col].values[0] 
+                original_df.at[i, col] = original_df.at[i, col] * (1-length_penalty) + diff*length_penalty
     # post_processing
     original_df = post_processing(original_df, model_len_info=None)
-    return original_df
-
+    return original_df 
 
 def load_benchdata():
     print("Loading WildBench data...")

model_info.json

@@ -4,7 +4,9 @@
     "Llama-2-7b-chat-hf.nosp": {"pretty_name": "Llama-2-7B-chat", "hf_model_id": "meta-llama/Llama-2-7b-chat-hf"},
     "Llama-2-7b-chat-hf": {"pretty_name": "Llama-2-7B-chat (+sys prmpt)", "hf_model_id": "meta-llama/Llama-2-7b-chat-hf"},
     "Mistral-7B-Instruct-v0.1": {"pretty_name": "Mistral-7B-Instruct", "hf_model_id": "mistralai/Mistral-7B-Instruct-v0.1"},
+    "Mistral-7B-Instruct-v0.2": {"pretty_name": "Mistral-7B-Instruct (v0.2)", "hf_model_id": "mistralai/Mistral-7B-Instruct-v0.2"},
     "Mixtral-8x7B-Instruct-v0.1": {"pretty_name": "Mixtral-8x7B-Instruct", "hf_model_id": "mistralai/Mixtral-8x7B-Instruct-v0.1"},
+    "Nous-Hermes-2-Mixtral-8x7B-DPO": {"pretty_name": "Nous-Hermes-2-Mixtral-8x7B-DPO", "hf_model_id": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO"},
     "Yi-34B-Chat": {"pretty_name": "Yi-34B-Chat", "hf_model_id": "01-ai/Yi-34B"},
     "gemini-1.0-pro": {"pretty_name": "gemini-1.0-pro", "hf_model_id": "https://blog.google/technology/ai/google-gemini-ai/"},
     "gemma-7b-it": {"pretty_name": "Gemma-7B-it", "hf_model_id": "google/gemma-7b"},
@@ -15,5 +17,6 @@
     "zephyr-7b-beta": {"pretty_name": "Zephyr-7b-beta", "hf_model_id": "HuggingFaceH4/zephyr-7b-beta"},
     "mistral-large-2402": {"pretty_name": "Mistral-Large", "hf_model_id": "https://mistral.ai/news/mistral-large/"},
     "claude-3-opus-20240229": {"pretty_name": "Claude 3 Opus", "hf_model_id": "https://www.anthropic.com/claude"},
-    "claude-3-sonnet-20240229": {"pretty_name": "Claude 3 Sonnet", "hf_model_id": "https://www.anthropic.com/claude"}
-}
+    "claude-3-sonnet-20240229": {"pretty_name": "Claude 3 Sonnet", "hf_model_id": "https://www.anthropic.com/claude"},
+    "zephyr-7b-gemma-v0.1": {"pretty_name": "Zephyr-7b-Gemma", "hf_model_id": "HuggingFaceH4/zephyr-7b-gemma-v0.1"}
+}

model_len_info.json

@@ -1,19 +1,102 @@
 {
-  "Llama-2-13b-chat-hf.nosp": 2943.346238938053,
-  "Llama-2-70b-chat-hf.nosp": 3077.0840707964603,
-  "Llama-2-7b-chat-hf.nosp": 2965.4059734513276,
-  "Llama-2-7b-chat-hf": 2137.34,
-  "Mistral-7B-Instruct-v0.1": 2208.8115234375,
-  "Mixtral-8x7B-Instruct-v0.1": 2483.9638671875,
-  "Yi-34B-Chat": 2899.1797133406835,
-  "gemini-1.0-pro": 2407.559462254395,
-  "gemma-7b-it": 1960.829244357213,
-  "gpt-3.5-turbo-0125": 1725.7216796875,
-  "gpt-4-0125-preview": 3190.716796875,
-  "tulu-2-dpo-70b": 2630.2337917485265,
-  "vicuna-13b-v1.5": 1864.2749445676275,
-  "zephyr-7b-beta": 3011.2529296875,
-  "mistral-large-2402": 2352.189453125,
-  "claude-3-opus-20240229": 2460.330078125,
-  "claude-3-sonnet-20240229": 2456.21484375
+  "Llama-2-13b-chat-hf.nosp": {
+    "avg_len": 2943.346238938053,
+    "empty_output": 120,
+    "num_samples": 1024
+  },
+  "Llama-2-70b-chat-hf.nosp": {
+    "avg_len": 3077.0840707964603,
+    "empty_output": 120,
+    "num_samples": 1024
+  },
+  "Llama-2-7b-chat-hf.nosp": {
+    "avg_len": 2965.4059734513276,
+    "empty_output": 120,
+    "num_samples": 1024
+  },
+  "Llama-2-7b-chat-hf": {
+    "avg_len": 2137.34,
+    "empty_output": 124,
+    "num_samples": 1024
+  },
+  "Mistral-7B-Instruct-v0.1": {
+    "avg_len": 2208.8115234375,
+    "empty_output": 0,
+    "num_samples": 1024
+  },
+  "Mistral-7B-Instruct-v0.2": {
+    "avg_len": 2852.33203125,
+    "empty_output": 0,
+    "num_samples": 1024
+  },
+  "Mixtral-8x7B-Instruct-v0.1": {
+    "avg_len": 2483.9638671875,
+    "empty_output": 0,
+    "num_samples": 1024
+  },
+  "Nous-Hermes-2-Mixtral-8x7B-DPO": {
+    "avg_len": 2878.79296875,
+    "empty_output": 0,
+    "num_samples": 1024
+  },
+  "Yi-34B-Chat": {
+    "avg_len": 2899.1797133406835,
+    "empty_output": 117,
+    "num_samples": 1024
+  },
+  "gemini-1.0-pro": {
+    "avg_len": 2407.559462254395,
+    "empty_output": 57,
+    "num_samples": 1024
+  },
+  "gemma-7b-it": {
+    "avg_len": 1960.829244357213,
+    "empty_output": 5,
+    "num_samples": 1024
+  },
+  "gpt-3.5-turbo-0125": {
+    "avg_len": 1725.7216796875,
+    "empty_output": 0,
+    "num_samples": 1024
+  },
+  "gpt-4-0125-preview": {
+    "avg_len": 3190.716796875,
+    "empty_output": 0,
+    "num_samples": 1024
+  },
+  "tulu-2-dpo-70b": {
+    "avg_len": 2630.2337917485265,
+    "empty_output": 6,
+    "num_samples": 1024
+  },
+  "vicuna-13b-v1.5": {
+    "avg_len": 1864.2749445676275,
+    "empty_output": 122,
+    "num_samples": 1024
+  },
+  "zephyr-7b-beta": {
+    "avg_len": 3011.2529296875,
+    "empty_output": 0,
+    "num_samples": 1024
+  },
+  "mistral-large-2402": {
+    "avg_len": 2352.189453125,
+    "empty_output": 0,
+    "num_samples": 1024
+  },
+  "claude-3-opus-20240229": {
+    "avg_len": 2460.330078125,
+    "empty_output": 0,
+    "num_samples": 1024
+  },
+  "claude-3-sonnet-20240229": {
+    "avg_len": 2456.21484375,
+    "empty_output": 0,
+    "num_samples": 1024
+  },
+  "zephyr-7b-gemma-v0.1": {
+    "avg_len": 2551.9842983316976,
+    "empty_output": 5,
+    "num_samples": 1024
+  }
 }