[Feature] support arenahard evaluation (#1096)

* support arenahard

* support arenahard

* support arenahard

README.md

@@ -70,6 +70,7 @@ Just like a compass guides us on our journey, OpenCompass will guide you through
 
 ## 🚀 What's New <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>
 
+- **\[2024.04.26\]** We supported the evaluation of [ArenaHard](configs/eval_subjective_arena_hard.py)  welcome to try!🔥🔥🔥.
 - **\[2024.04.22\]** We supported the evaluation of [LLaMA3](configs/models/hf_llama/hf_llama3_8b.py) 和 [LLaMA3-Instruct](configs/models/hf_llama/hf_llama3_8b_instruct.py), welcome to try! 🔥🔥🔥
 - **\[2024.02.29\]** We supported the MT-Bench, AlpacalEval and AlignBench, more information can be found [here](https://opencompass.readthedocs.io/en/latest/advanced_guides/subjective_evaluation.html)
 - **\[2024.01.30\]** We release OpenCompass 2.0. Click  [CompassKit](https://github.com/open-compass), [CompassHub](https://hub.opencompass.org.cn/home), and [CompassRank](https://rank.opencompass.org.cn/home) for more information !

README_zh-CN.md

@@ -69,6 +69,7 @@
 
 ## 🚀 最新进展 <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>
 
+- **\[2024.04.26\]** 我们支持了 [ArenaHard评测](configs/eval_subjective_arena_hard.py) 欢迎试用！🔥🔥🔥.
 - **\[2024.04.22\]** 我们支持了 [LLaMA3](configs/models/hf_llama/hf_llama3_8b.py) 和 [LLaMA3-Instruct](configs/models/hf_llama/hf_llama3_8b_instruct.py) 的评测，欢迎试用！🔥🔥🔥.
 - **\[2024.02.29\]** 我们支持了MT-Bench、AlpacalEval和AlignBench，更多信息可以在[这里](https://opencompass.readthedocs.io/en/latest/advanced_guides/subjective_evaluation.html)找到。
 - **\[2024.01.30\]** 我们发布了OpenCompass 2.0。更多信息，请访问[CompassKit](https://github.com/open-compass)、[CompassHub](https://hub.opencompass.org.cn/home)和[CompassRank](https://rank.opencompass.org.cn/home)。

configs/datasets/subjective/arena_hard/README.md

@@ -0,0 +1,40 @@
+# ArenaHard
+## Introduction
+The following introduction comes from the official repo:
+Arena-Hard is an evaluation tool for instruction-tuned LLMs. It contains 500 challenging user queries, which prompt GPT-4-Turbo as judge to compare the models' responses against a baseline model (default: GPT-4-0314).
+
+## Official link
+https://github.com/lm-sys/arena-hard
+
+### Paper
+https://lmsys.org/blog/2024-04-19-arena-hard/
+
+## Examples
+Input example I:
+```
+Use ABC notation to write a melody in the style of a folk tune.
+```
+
+Output example I (from GPT-4):
+```
+X:1\nT:Untitled Folk Tune\nM:4/4\nL:1/8\nK:G\n|:G2A2|B2A2|G2E2|D4|E2F2|G2F2|E2C2|B,4|\nA2B2|c2B2|A2F2|E4|D2E2|F2E2|D2B,2|C4:|
+```
+
+
+## Evaluation results
+
+```
+LLaMa3-8b-instruct: 20.6 (Official Results)
+LLaMa3-8b-instruct: 21.9 (Opencompass Results)
+```
+
+## Reference
+```
+@misc{arenahard2024,
+    title = {From Live Data to High-Quality Benchmarks: The Arena-Hard Pipeline},
+    url = {https://lmsys.org/blog/2024-04-19-arena-hard/},
+    author = {Tianle Li*, Wei-Lin Chiang*, Evan Frick, Lisa Dunlap, Banghua Zhu, Joseph E. Gonzalez, Ion Stoica},
+    month = {April},
+    year = {2024}
+}
+```

configs/datasets/subjective/arena_hard/arena_hard_scoring.py

@@ -0,0 +1,72 @@
+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import GenInferencer
+from opencompass.openicl.icl_evaluator import LMEvaluator
+from opencompass.datasets import ArenaHardDataset
+from mmengine.config import read_base
+
+subjective_reader_cfg = dict(
+    input_columns=['question'],
+    output_column='judge',
+    )
+
+subjective_all_sets = [
+    "question",
+]
+
+
+subjective_datasets = []
+
+system_prompt = "Please act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user prompt displayed below. You will be given assistant A's answer and assistant B's answer. Your job is to evaluate which assistant's answer is better.\n\nBegin your evaluation by generating your own answer to the prompt. You must provide your answers before judging any answers.\n\nWhen evaluating the assistants' answers, compare both assistants' answers with your answer. You must identify and correct any mistakes or inaccurate information.\n\nThen consider if the assistant's answers are helpful, relevant, and concise. Helpful means the answer correctly responds to the prompt or follows the instructions. Note when user prompt has any ambiguity or more than one interpretation, it is more helpful and appropriate to ask for clarifications or more information from the user than providing an answer based on assumptions. Relevant means all parts of the response closely connect or are appropriate to what is being asked. Concise means the response is clear and not verbose or excessive.\n\nThen consider the creativity and novelty of the assistant's answers when needed. Finally, identify any missing important information in the assistants' answers that would be beneficial to include when responding to the user prompt.\n\nAfter providing your explanation, you must output only one of the following choices as your final verdict with a label:\n\n1. Assistant A is significantly better: [[A>>B]]\n2. Assistant A is slightly better: [[A>B]]\n3. Tie, relatively the same: [[A=B]]\n4. Assistant B is slightly better: [[B>A]]\n5. Assistant B is significantly better: [[B>>A]]\n\nExample output: \"My final verdict is tie: [[A=B]]\"."
+
+judge_prompt = "<|User Prompt|>\n{question}\n\n<|The Start of Assistant A's Answer|>\n{prediction}\n<|The End of Assistant A's Answer|>\n\n<|The Start of Assistant B's Answer|>\n{prediction2}\n<|The End of Assistant B's Answer|>"
+
+
+for _name in subjective_all_sets:
+    subjective_infer_cfg = dict(
+            prompt_template=dict(
+                type=PromptTemplate,
+                template=dict(round=[
+                    dict(
+                        role='HUMAN',
+                        prompt="{question}"
+                    ),
+                ]),
+            ),
+            retriever=dict(type=ZeroRetriever),
+            inferencer=dict(type=GenInferencer, max_out_len=4096),
+        )
+
+    subjective_eval_cfg = dict(
+        evaluator=dict(
+            type=LMEvaluator,
+            prompt_template=dict(
+                type=PromptTemplate,
+                template=dict(
+                begin=[
+                    dict(
+                        role='SYSTEM',
+                        fallback_role='HUMAN',
+                        prompt=system_prompt)
+                ],
+                    round=[
+                    dict(
+                        role='HUMAN',
+                        prompt = judge_prompt
+                    ),
+                ]),
+            ),
+        ),
+        pred_role="BOT",
+    )
+
+    subjective_datasets.append(
+        dict(
+            abbr=f"{_name}",
+            type=ArenaHardDataset,
+            path="./data/subjective/arena_hard",
+            name=_name,
+            reader_cfg=subjective_reader_cfg,
+            infer_cfg=subjective_infer_cfg,
+            eval_cfg=subjective_eval_cfg
+        ))

configs/eval_subjective_arena_hard.py

@@ -0,0 +1,104 @@
+from opencompass.models import HuggingFaceCausalLM
+from copy import deepcopy
+from opencompass.models import TurboMindModel
+from mmengine.config import read_base
+
+from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3, OpenAI
+from opencompass.partitioners import NaivePartitioner, SizePartitioner
+from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
+from opencompass.partitioners.sub_size import SubjectiveSizePartitioner
+from opencompass.runners import LocalRunner
+from opencompass.runners import SlurmSequentialRunner
+from opencompass.tasks import OpenICLInferTask
+from opencompass.tasks.subjective_eval import SubjectiveEvalTask
+from opencompass.summarizers import ArenaHardSummarizer
+
+with read_base():
+    from .datasets.subjective.arena_hard.arena_hard_scoring import subjective_datasets
+
+api_meta_template = dict(
+    round=[
+        dict(role='HUMAN', api_role='HUMAN'),
+        dict(role='BOT', api_role='BOT', generate=True),
+    ]
+)
+
+_meta_template = dict(
+    round=[
+        dict(role="HUMAN", begin="<|begin_of_text|>user<|end_header_id|>\n\n", end="<|eot_id|>"),
+        dict(role="BOT", begin="<|begin_of_text|>assistant<|end_header_id|>\n\n", end="<|eot_id|>", generate=True),
+    ],
+)
+
+models = [
+    dict(
+        type=HuggingFaceCausalLM,
+        abbr="llama-3-8b-instruct-hf",
+        path="meta-llama/Meta-Llama-3-8B-Instruct",
+        model_kwargs=dict(device_map="auto"),
+        tokenizer_kwargs=dict(
+            padding_side="left",
+            truncation_side="left",
+            use_fast=False,
+        ),
+        meta_template=_meta_template,
+        max_out_len=4096,
+        max_seq_len=2048,
+        batch_size=8,
+        run_cfg=dict(num_gpus=1, num_procs=1),
+        generation_kwargs={"eos_token_id": [128001, 128009]},
+        batch_padding=True,
+    )
+]
+
+datasets = [*subjective_datasets]
+
+work_dir = 'outputs/arena_hard/'
+# -------------Inferen Stage ----------------------------------------
+
+
+infer = dict(
+    partitioner=dict(type=SizePartitioner, max_task_size=1000000),
+    runner=dict(
+        type=LocalRunner,
+        max_num_workers=32,
+        task=dict(type=OpenICLInferTask)),
+)
+
+judge_models = [dict(
+    abbr='GPT4-Turbo',
+    type=OpenAI,
+    path='gpt-4-1106-preview',
+    key='',  
+        meta_template=api_meta_template,
+        query_per_second=1,
+        max_out_len=1024,
+        max_seq_len=4096,
+        batch_size=10,
+        retry=10,
+        temperature = 0,
+)]
+
+## ------------- Evaluation Configuration
+gpt4_0314 = dict(
+    abbr='gpt4-0314',
+    type=OpenAI,
+)
+
+eval = dict(
+    partitioner=dict(
+        type=SubjectiveSizePartitioner,
+        max_task_size=1000000,
+        mode='m2n',
+        infer_order='double',
+        base_models=[gpt4_0314],
+        compare_models=models,
+        judge_models=judge_models,
+    ),
+    runner=dict(type=LocalRunner, max_num_workers=16, task=dict(type=SubjectiveEvalTask)),
+    given_pred = [{'abbr':'gpt4-0314', 'path':''}]
+)
+
+summarizer = dict(
+    type=ArenaHardSummarizer
+)

docs/en/advanced_guides/subjective_evaluation.md

@@ -15,10 +15,11 @@ We support the use of GPT-4 (or other JudgeLLM) for the subjective evaluation of
 
 ## Current Supported Subjective Evaluation Datasets
 
-1. AlginBench (https://github.com/THUDM/AlignBench)
+1. AlignBench (https://github.com/THUDM/AlignBench)
 2. MTBench (https://github.com/lm-sys/FastChat)
 3. AlpacaEvalv2 (https://github.com/tatsu-lab/alpaca_eval)
-4. CompassArena (Internal dataset)
+4. ArenaHard (https://github.com/lm-sys/arena-hard/tree/main)
+5. CompassArena (Internal dataset)
 
 ## Subjective Evaluation with Custom Dataset
 

docs/zh_cn/advanced_guides/subjective_evaluation.md

@@ -15,10 +15,11 @@
 
 ## 目前已支持的主观评测数据集
 
-1. AlginBench（https://github.com/THUDM/AlignBench）
+1. AlignBench（https://github.com/THUDM/AlignBench）
 2. MTBench （https://github.com/lm-sys/FastChat）
 3. AlpacaEvalv2 （https://github.com/tatsu-lab/alpaca_eval）
-4. CompassArena（内部数据集）
+4. ArenaHard (https://github.com/lm-sys/arena-hard/tree/main)
+5. CompassArena（内部数据集）
 
 ## 自定义主观数据集评测
 

opencompass/datasets/subjective/__init__.py

@@ -1,4 +1,5 @@
 from .alignbench import AlignmentBenchDataset  # noqa: F401, F403
+from .arena_hard import ArenaHardDataset  # noqa: F401, F403
 from .compass_arena import CompassArenaDataset  # noqa: F401, F403
 from .corev2 import Corev2Dataset  # noqa: F401, F403
 from .creationbench import CreationBenchDataset  # noqa: F401, F403

opencompass/datasets/subjective/arena_hard.py

@@ -0,0 +1,35 @@
+import json
+import os.path as osp
+
+from datasets import Dataset, DatasetDict
+
+from opencompass.registry import LOAD_DATASET
+
+from ..base import BaseDataset
+
+
+@LOAD_DATASET.register_module()
+class ArenaHardDataset(BaseDataset):
+
+    def load(self, path: str, name: str):
+        filename = osp.join(path, f'{name}.jsonl')
+        dataset = DatasetDict()
+        raw_data = []
+        with open(filename, 'r', encoding='utf-8') as file:
+            for line in file:
+                problem = json.loads(line)
+                question_id = problem['question_id']
+                cluster = problem['cluster']
+                question = problem['turns'][0][
+                    'content']  # only one turn in arena_hard
+                raw_data.append({
+                    'question': question,
+                    'capability': cluster,
+                    'judge': {
+                        'capability': cluster,
+                        'question': question,
+                        'question_id': question_id
+                    }
+                })
+        dataset = Dataset.from_list(raw_data)
+        return dataset

opencompass/summarizers/subjective/__init__.py

@@ -2,6 +2,7 @@
 from .alignmentbench import AlignmentBenchSummarizer
 from .all_obj import AllObjSummarizer
 from .alpacaeval import AlpacaSummarizer
+from .arenahard import ArenaHardSummarizer
 from .compass_arena import CompassArenaSummarizer
 from .corev2 import Corev2Summarizer
 from .creationbench import CreationBenchSummarizer

opencompass/summarizers/subjective/arenahard.py

@@ -0,0 +1,311 @@
+# flake8: noqa
+# yapf: disable
+import argparse
+import datetime
+import json
+import math
+import os
+import os.path as osp
+import re
+from collections import defaultdict
+from datetime import datetime
+from glob import glob
+from itertools import product
+
+import mmengine
+import numpy as np
+#import plotly.express as px
+import pandas as pd
+import tiktoken
+from mmengine import ConfigDict
+from sklearn.linear_model import LogisticRegression
+from tabulate import tabulate
+from tqdm import tqdm
+
+from opencompass.partitioners.sub_naive import remove_duplicate_pairs
+from opencompass.utils import dataset_abbr_from_cfg, model_abbr_from_cfg
+
+from .utils import get_outdir
+
+
+def compute_mle_elo(df, SCALE=400, BASE=10, INIT_RATING=1000):
+    models = pd.concat([df['model_a'], df['model_b']]).unique()
+    models = pd.Series(np.arange(len(models)), index=models)
+
+    # duplicate battles
+    df = pd.concat([df, df], ignore_index=True)
+    p = len(models.index)
+    n = df.shape[0]
+
+    X = np.zeros([n, p])
+    X[np.arange(n), models[df['model_a']]] = +math.log(BASE)
+    X[np.arange(n), models[df['model_b']]] = -math.log(BASE)
+
+    # one A win => two A win
+    Y = np.zeros(n)
+    Y[df['winner'] == 'model_a'] = 1.0
+
+    # one tie => one A win + one B win
+    # find tie + tie (both bad) index
+    tie_idx = (df['winner'] == 'tie') | (df['winner'] == 'tie (bothbad)')
+    tie_idx[len(tie_idx)//2:] = False
+    Y[tie_idx] = 1.0
+
+    lr = LogisticRegression(fit_intercept=False, penalty=None, tol=1e-8)
+    lr.fit(X,Y)
+
+    elo_scores = SCALE * lr.coef_[0] + INIT_RATING
+
+    # set anchor as gpt4-0314 = 1000
+    if 'gpt4-0314' in models.index:
+        elo_scores += 1000 - elo_scores[models['gpt4-0314']]
+    return pd.Series(elo_scores, index = models.index).sort_values(ascending=False)
+
+
+def get_bootstrap_result(battles, func_compute_elo, num_round):
+    rows = []
+    for i in tqdm(range(num_round), desc='bootstrap'):
+        rows.append(func_compute_elo(battles.sample(frac=1.0, replace=True)))
+    df = pd.DataFrame(rows)
+    return df[df.median().sort_values(ascending=False).index]
+
+
+def preety_print_two_ratings(ratings_1, ratings_2, column_names):
+    df = pd.DataFrame([
+        [n, ratings_1[n], ratings_2[n]] for n in ratings_1.keys()
+    ], columns=['Model', column_names[0], column_names[1]]).sort_values(column_names[0], ascending=False).reset_index(drop=True)
+    df[column_names[0]] = (df[column_names[0]] + 0.5).astype(int)
+    df[column_names[1]] = (df[column_names[1]] + 0.5).astype(int)
+    df.index = df.index + 1
+    return df
+
+
+def visualize_bootstrap_scores(df, title):
+    bars = pd.DataFrame(dict(
+        lower = df.quantile(.025),
+        rating = df.quantile(.5),
+        upper = df.quantile(.975))).reset_index(names='model').sort_values('rating', ascending=False)
+    bars['error_y'] = bars['upper'] - bars['rating']
+    bars['error_y_minus'] = bars['rating'] - bars['lower']
+    bars['rating_rounded'] = np.round(bars['rating'], 2)
+    fig = px.scatter(bars, x='model', y='rating', error_y='error_y',
+                     error_y_minus='error_y_minus', text='rating_rounded',
+                     title=title)
+    fig.update_layout(xaxis_title='Model', yaxis_title='Rating',
+                      height=600)
+    return fig
+
+
+def predict_win_rate(elo_ratings, SCALE=400, BASE=10, INIT_RATING=1000):
+    names = sorted(list(elo_ratings.keys()))
+    wins = defaultdict(lambda: defaultdict(lambda: 0))
+    for a in names:
+        for b in names:
+            ea = 1 / (1 + BASE ** ((elo_ratings[b] - elo_ratings[a]) / SCALE))
+            wins[a][b] = ea
+            wins[b][a] = 1 - ea
+
+    data = {
+        a: [wins[a][b] if a != b else np.NAN for b in names]
+        for a in names
+    }
+
+    df = pd.DataFrame(data, index=names)
+    df.index.name = 'model_a'
+    df.columns.name = 'model_b'
+    return df.T
+
+
+def model_abbr_from_cfg_used_in_summarizer(model):
+    if model.get('summarizer_abbr', None):
+        return model['summarizer_abbr']
+    else:
+        return model_abbr_from_cfg(model)
+
+def post_process_compass_arena(s):
+    if result := re.findall('\[\[([AB<>=]+)\]\]', s):
+        return result[0]
+    else:
+        return None
+
+def get_win_rate_column(df, column, baseline='gpt4-0314'):
+    to_dict = df[['model', column]].set_index('model').to_dict()[column]
+    win_rate_table = predict_win_rate(to_dict)
+    return win_rate_table[baseline].fillna(0.5).apply(lambda x: round(x * 100, 2))
+
+
+def get_battles_from_judgment(dataset, subdir_path, post_process, first_game_only=False, WEIGHT=3):
+    arena_hard_battles = pd.DataFrame()
+    print('Turning judgment results into battles...')
+
+    dataset_abbr = dataset_abbr_from_cfg(dataset)
+    filename = osp.join(subdir_path, dataset_abbr + '.json')
+    partial_filename = osp.join(subdir_path, dataset_abbr + '_0.json')
+    if osp.exists(osp.realpath(filename)):
+        result = mmengine.load(filename)
+    elif osp.exists(osp.realpath(partial_filename)):
+        filename = partial_filename
+        result = {}
+        i = 1
+        partial_dict_flag = 0
+        while osp.exists(osp.realpath(filename)):
+            res = mmengine.load(filename)
+            for k, v in res.items():
+                result[partial_dict_flag] = v
+                partial_dict_flag += 1
+            filename = osp.join(subdir_path,
+                                dataset_abbr + '_' + str(i) + '.json')
+            i += 1
+    else:
+        result = {}
+
+    if len(result) == 0:
+        print('*' * 100)
+        print('There are no results for ' + filename + ' or ' +
+              partial_filename)
+        print('*' * 100)
+        assert len(result) > 0
+
+    judged_answers = []
+    references = []
+    for k, v in result.items():
+
+        output = {
+                'model_a': v['gold']['answer1'],
+                'model_b': v['gold']['answer2']}
+
+        processed_judge = post_process(v['prediction'])
+        if processed_judge is not None:
+            weight = 1
+            if processed_judge == 'A=B':
+                output['winner'] = 'tie'
+            elif processed_judge == 'A>B':
+                output['winner'] = 'model_a'
+            elif processed_judge == 'A>>B':
+                output['winner'] = 'model_a'
+                weight = WEIGHT
+            elif processed_judge == 'B>A':
+                output['winner'] = 'model_b'
+            elif processed_judge == 'B>>A':
+                output['winner'] = 'model_b'
+                weight = WEIGHT
+            else:
+                weight = 0
+        else:
+            weight = 0
+
+        if weight:
+            arena_hard_battles = pd.concat([arena_hard_battles, pd.DataFrame([output] * weight)])
+
+    arena_hard_battles.to_json(os.path.join(subdir_path,'arena_hard_battles.jsonl'), lines=True, orient='records')
+    return arena_hard_battles
+
+class ArenaHardSummarizer:
+    """Do the subjectivity analyze based on evaluation results.
+
+    Args:
+        config (ConfigDict): The configuration object of the evaluation task.
+            It's expected to be filled out at runtime.
+    """
+
+    def __init__(self,
+                 config: ConfigDict,
+                 judge_type='general',
+                 check_pos_bias=True,
+                 summary_type='single') -> None:
+        self.tasks = []
+        self.cfg = config
+        self.base_models = self.cfg['eval']['partitioner']['base_models']
+        self.compare_models = self.cfg['eval']['partitioner']['compare_models']
+        self.judge_models = self.cfg.get('judge_models', None)
+        self.meta_judge_model = self.cfg.eval.partitioner.get('meta_judge_model', None)
+        self.judge_type = judge_type
+        assert self.judge_type in ['general']
+        self.judge_map = {'general': post_process_compass_arena}
+        self.judge_function = self.judge_map[self.judge_type]
+        self.check_pos_bias = check_pos_bias
+        self.summary_type = summary_type
+
+    def get_score(self, time_str):
+        output_dir, results_folder = get_outdir(self.cfg, time_str)
+        model_combinations = list(product(self.base_models, self.compare_models))
+        unique_combinations = remove_duplicate_pairs([combo for combo in model_combinations if combo[0] != combo[1]])
+
+        if self.meta_judge_model is not None:
+            self.judge_models.append(self.meta_judge_model)
+
+        scores = {}
+
+        for idx, judge_model_cfg in enumerate(self.judge_models):
+            judge_model = model_abbr_from_cfg(judge_model_cfg)
+            for dataset in self.cfg['datasets']:
+                dataset_abbr = dataset_abbr_from_cfg(dataset)
+                for model_pair in unique_combinations:
+                    model1 = model_pair[0]['abbr']
+                    model2 = model_pair[1]['abbr']
+                    if idx == len(self.judge_models):
+                        subdir = model1 + '_' + model2 + '_summarized-by--' + judge_model
+                    else:
+                        subdir = model1 + '_' + model2 + '_judged-by--' + judge_model
+                    subdir_path = os.path.join(results_folder, subdir)
+                    if not os.path.isdir(subdir_path):
+                        print(subdir_path + ' is not exist! please check!')
+                        continue
+
+                    battles = get_battles_from_judgment(dataset, subdir_path, self.judge_function)
+
+                    bootstrap_online_elo = compute_mle_elo(battles)
+
+                    np.random.seed(42)
+                    bootstrap_elo_lu = get_bootstrap_result(battles, compute_mle_elo, 100)
+                    bootstrap_elo_lu.to_json(os.path.join(subdir_path,'bootstrapping_results.jsonl'), lines=True, orient='records')
+
+                    stats = pd.DataFrame()
+                    stats['results'] = None
+                    stats['results'] = stats['results'].astype('object')
+
+                    for i, model in enumerate(bootstrap_online_elo.index):
+                        assert model in bootstrap_elo_lu.columns
+
+                        stats.at[i, 'model'] = model
+                        stats.at[i, 'score'] = bootstrap_online_elo[model]
+                        stats.at[i, 'lower'] = np.percentile(bootstrap_elo_lu[model], 2.5)
+                        stats.at[i, 'upper'] = np.percentile(bootstrap_elo_lu[model], 97.5)
+                        if model == 'gpt4-0314':
+                            stats.at[i, 'avg_tokens'] = 423
+                        else:
+                            with open(os.path.join(output_dir.split('summary')[0], 'predictions', model, dataset_abbr+'.json'), 'r') as f:
+                                model_preds = json.load(f)
+                                pred_length = 0
+                                for k, v in model_preds.items():
+                                    pred_length += len(tiktoken.encoding_for_model('gpt-3.5-turbo').encode(v['prediction']))
+                                pred_length /= len(model_preds)
+                            stats.at[i, 'avg_tokens'] = pred_length
+                        stats.at[i, 'results'] = bootstrap_elo_lu[model].tolist()
+                    stats.sort_values(by='model', inplace=True)
+                    stats['score'] = get_win_rate_column(stats, 'score', 'gpt4-0314').tolist()
+                    stats['lower'] = get_win_rate_column(stats, 'lower', 'gpt4-0314').tolist()
+                    stats['upper'] = get_win_rate_column(stats, 'upper', 'gpt4-0314').tolist()
+                    decimal = 1
+
+
+                    stats.sort_values(by='score', ascending=False, inplace=True)
+                    for _, row in stats.iterrows():
+                        interval = str((round(row['lower'] - row['score'], decimal), round(row['upper'] - row['score'], decimal)))
+                        print(f"{row['model'] : <30} | score: {round(row['score'], decimal) : ^5} | 95% CI: {interval : ^12} | average #tokens: {int(row['avg_tokens'])}")
+
+                    stats.to_json(os.path.join(output_dir,'arena_hard_leaderboard.json'), orient='records', indent=4)
+
+    def summarize(
+            self,
+            time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S'),
+    ):
+        """Summarize the subjectivity analysis based on evaluation results.
+
+        Args:
+            time_str (str): Timestamp for file naming.
+
+        Returns:
+            pd.DataFrame: The summary results.
+        """
+        self.get_score(time_str)