import glob import os from collections import defaultdict from typing import Any, List from evalscope.benchmarks import Benchmark, DataAdapter from evalscope.constants import EvalType from evalscope.metrics import Metric, mean, metric_registry from evalscope.report import Report, ReportKey from evalscope.utils.logger import get_logger # flake8: noqa logger = get_logger() GRADER_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]]\"." # noqa: E501 GRADER_TEMPLATE = "<|User Prompt|>\n{question}\n\n<|The Start of Assistant A's Answer|>\n{answer_1}\n<|The End of Assistant A's Answer|>\n\n<|The Start of Assistant B's Answer|>\n{answer_2}\n<|The End of Assistant B's Answer|>".strip( ) # noqa: E501 @Benchmark.register( name='general_arena', pretty_name='GeneralArena', tags=['Custom', 'Arena'], description= 'GeneralArena is a custom benchmark designed to evaluate the performance of large language models in a competitive setting, ' 'where models are pitted against each other in custom tasks to determine their relative strengths and weaknesses. You should ' 'provide the model outputs in the format of a list of dictionaries, where each dictionary contains the model name and its report path. ' 'For detailed instructions on how to use this benchmark, please refer to the [Arena User Guide](https://evalscope.readthedocs.io/zh-cn/latest/user_guides/arena.html).', dataset_id='general_arena', metric_list=['winrate'], few_shot_num=0, train_split=None, eval_split='test', system_prompt=GRADER_SYSTEM_PROMPT, prompt_template=GRADER_TEMPLATE, extra_params={ 'models': [{ 'name': 'qwen-plus', 'report_path': 'outputs/20250627_172550/reports/qwen-plus' }, { 'name': 'qwen2.5-7b', 'report_path': 'outputs/20250627_172817/reports/qwen2.5-7b-instruct' }], 'baseline': 'qwen2.5-7b' }) class GeneralArenaAdapter(DataAdapter): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # register metrics metric_registry.register(Metric(name='winrate', object=mean)) # whether to use LLM as a judge self.llm_as_a_judge = True extra_params = kwargs.get('extra_params', {}) self.models = extra_params.get('models', []) self.baseline = extra_params.get('baseline', None) def load(self, **kwargs): self._check_names() self._check_reports() self._check_datasets() logger.info(f'Overall datasets: {self.overall_datasets}') dataset_model_dict = self._load_common_datasets() data_dict = self._build_pair_wise_data(dataset_model_dict) return data_dict def gen_prompt(self, input_d, subset_name, few_shot_list, **kwargs): return self.gen_prompt_data(input_d['question']) def _check_names(self): """Check the names of the models and baseline.""" # check duplicate models model_names = [model['name'] for model in self.models] if len(model_names) != len(set(model_names)): raise ValueError(f'Duplicate model names found in the models list {model_names}.') # check if models list is empty if len(self.models) < 2: raise ValueError('Models list must contain at least two models.') # check baseline model if self.baseline and self.baseline not in model_names: raise ValueError(f'Baseline model {self.baseline} not found in the models list.') # check if the baseline model is not set if not self.baseline: logger.warning('Baseline model is not set. Using the first model as the baseline.') self.baseline = self.models[0]['name'] def _check_reports(self): """Check if the report paths are valid.""" for model in self.models: report_path = model.get('report_path', None) if not report_path or not os.path.exists(report_path): raise ValueError(f'Report path {report_path} for model {model["name"]} does not exist.') reports = [] for report_item in glob.glob(os.path.join(report_path, '*.json')): report = Report.from_json(report_item) reports.append(report) model['reports'] = reports def _check_datasets(self): """Check common datasets in the reports.""" overall_datasets = set() for model in self.models: datasets = set() for report in model['reports']: report_df = report.to_dataframe() # get unique (dataset, subset) tuples unique_datasets = set(zip(report_df[ReportKey.dataset_name], report_df[ReportKey.subset_name])) datasets.update(unique_datasets) model['datasets'] = datasets # get overall datasets by intersecting all models' datasets overall_datasets = set.intersection(*[model['datasets'] for model in self.models if 'datasets' in model]) self.overall_datasets = overall_datasets def _load_common_datasets(self): """Load common datasets from the local path.""" from evalscope.utils import OutputsStructure, jsonl_to_list dataset_dict = defaultdict(dict) for dataset_name, subset_name in self.overall_datasets: for model in self.models: dataset_path = model['report_path'].replace(OutputsStructure.REPORTS_DIR, OutputsStructure.REVIEWS_DIR) dataset_file_path = os.path.join(dataset_path, f'{dataset_name}_{subset_name}.jsonl') if not os.path.exists(dataset_file_path): raise ValueError( f'Dataset {dataset_name} with subset {subset_name} not found in model {model["name"]}.') dataset = jsonl_to_list(dataset_file_path) # sort by index dataset.sort(key=lambda x: x.get('index')) dataset_dict[(dataset_name, subset_name)][model['name']] = dataset return dataset_dict def _build_pair_wise_data(self, dataset_dict): """Build pairwise data for the models.""" from .utils import process_review_item pairwise_data = defaultdict(dict) for (dataset_name, subset_name), model_data in dataset_dict.items(): if len(model_data) < 2: logger.warning(f'Not enough models for dataset {dataset_name} with subset {subset_name}. Skipping.') continue # create pairwise data for each model against the baseline model_names = list(model_data.keys()) for name in model_names: if name == self.baseline: continue pairs = [] for model_item, baseline_item in zip(model_data[name], model_data[self.baseline]): for model_choice, baseline_choice in zip( process_review_item(model_item), process_review_item(baseline_item)): pairs.append({ 'question': model_choice['Question'], 'answer_1': model_choice['Generated'], 'answer_2': baseline_choice['Generated'], 'model_1': name, 'model_2': self.baseline }) pairwise_data[f'{dataset_name}&{subset_name}@{name}&{self.baseline}'][self.eval_split] = pairs return pairwise_data def llm_match(self, gold, pred, judge=None, **kwargs): from .utils import get_judge_score, post_process_result try: raw_input = kwargs.get('raw_input', None) question = raw_input['question'] answer_1 = raw_input['answer_1'] answer_2 = raw_input['answer_2'] model_1 = raw_input['model_1'] model_2 = raw_input['model_2'] except KeyError as e: logger.error(f'Missing key in raw input: {e}. Raw input: {raw_input}') raise system_template = self.system_prompt prompt_template = self.prompt_template prompt1 = prompt_template.format(question=question, answer_1=answer_1, answer_2=answer_2) # reverse the order prompt2 = prompt_template.format(question=question, answer_1=answer_2, answer_2=answer_1) # get grading response game1_response = judge(prompt1, system_prompt=system_template) game2_response = judge(prompt2, system_prompt=system_template) # parse grading response # game1 res1 = post_process_result(game1_response) score1 = get_judge_score(res1, reverse=False) # game2 res2 = post_process_result(game2_response) score2 = get_judge_score(res2, reverse=True) return { 'score': mean([score1, score2]), 'games': [ { 'model_a': model_1, 'model_b': model_2, 'response': game1_response, 'judgment': res1 }, { 'model_a': model_2, 'model_b': model_1, 'response': game2_response, 'judgment': res2 }, ] } def compute_metric(self, review_res_list: List[dict], **kwargs) -> List[dict]: """ compute score of the model """ import numpy as np import pandas as pd from .utils import compute_mle_elo, get_battles_from_row, get_bootstrap_result, get_win_rate_column if isinstance(review_res_list[0], list): review_res_list = [item for sublist in review_res_list for item in sublist] battles = pd.concat([get_battles_from_row(res) for res in review_res_list]) bt_model_coef = compute_mle_elo(battles, baseline_model=self.baseline) bootstrap_model_coef = get_bootstrap_result( battles, func_compute_elo=compute_mle_elo, num_round=100, baseline_model=self.baseline) stats = pd.DataFrame() stats['results'] = None stats['results'] = stats['results'].astype('object') for i, model in enumerate(bt_model_coef.index): # assert model in bootstrap_elo_lu.columns stats.at[i, 'model'] = model stats.at[i, 'score'] = bt_model_coef[model] stats.at[i, 'lower'] = np.percentile(bootstrap_model_coef[model], 2.5) stats.at[i, 'upper'] = np.percentile(bootstrap_model_coef[model], 97.5) metrics_dict = {} metrics_dict['winrate'] = get_win_rate_column(stats, 'score', self.baseline).to_dict() metrics_dict['winrate_lower'] = get_win_rate_column(stats, 'lower', self.baseline).to_dict() metrics_dict['winrate_upper'] = get_win_rate_column(stats, 'upper', self.baseline).to_dict() metrics = [] for metric_name, models in metrics_dict.items(): for model_name, score in models.items(): if model_name == self.baseline: continue metrics.append({'metric_name': metric_name, 'score': score, 'num': len(review_res_list)}) return metrics def post_process_report(self, report: 'Report', **kwargs): """Post-process the report to convert it to a DataFrame with winrate leaderboards.""" import pandas as pd import tabulate report_path = kwargs.get('report_path') leaderboard_file = os.path.join(report_path, 'leaderboard.txt') # Ensure report directory exists os.makedirs(report_path, exist_ok=True) # Convert report to dataframe df = report.to_dataframe() # Filter for winrate-related metrics winrate_df = df[df[ReportKey.metric_name].str.contains('winrate')].copy() if winrate_df.empty: logger.warning('No winrate data found in the report.') return # Get all model names from self.models all_model_names = [model['name'] for model in self.models] # Collect all leaderboard outputs leaderboard_outputs = [] def format_leaderboard(data_df, title): """Format DataFrame as leaderboard with CI.""" # Pivot to get winrate, winrate_lower, winrate_upper as columns pivot_df = data_df.pivot_table( index=[ReportKey.model_name], columns=ReportKey.metric_name, values=ReportKey.score, aggfunc='first') # Add baseline model with 50% winrate baseline_data = {'winrate': 0.5, 'winrate_lower': 0.5, 'winrate_upper': 0.5} # Create a complete index with all models complete_index = pd.Index(all_model_names, name=pivot_df.index.name) pivot_df = pivot_df.reindex(complete_index) # Fill baseline model data if self.baseline in pivot_df.index: for col, val in baseline_data.items(): if col in pivot_df.columns: pivot_df.loc[self.baseline, col] = val # Fill missing values with winrate score for other models if 'winrate' in pivot_df.columns: pivot_df['winrate_lower'] = pivot_df.get('winrate_lower', pivot_df['winrate']) pivot_df['winrate_upper'] = pivot_df.get('winrate_upper', pivot_df['winrate']) # Format for display leaderboard_data = [] for model in pivot_df.index: if pd.isna(pivot_df.loc[model, 'winrate']): continue score_pct = pivot_df.loc[model, 'winrate'] * 100 lower_diff = (pivot_df.loc[model, 'winrate_lower'] - pivot_df.loc[model, 'winrate']) * 100 upper_diff = (pivot_df.loc[model, 'winrate_upper'] - pivot_df.loc[model, 'winrate']) * 100 leaderboard_data.append({ 'Model': model, 'WinRate (%)': f'{score_pct:.1f}', 'CI (%)': f'({lower_diff:+.1f} / {upper_diff:+.1f})' }) # Sort by score descending leaderboard_data.sort(key=lambda x: float(x['WinRate (%)'].replace('%', '')), reverse=True) # Create DataFrame leaderboard_df = pd.DataFrame(leaderboard_data) leaderboard_df.index = range(len(leaderboard_df)) # Format as string table_str = tabulate.tabulate(leaderboard_df, headers='keys', showindex=False) output = f'{title}\n{table_str}\n' logger.info(f'\n{title}\n{table_str}') return output # Parse dataset and subset information from dataset_name column # Format: '{dataset_name}&{subset_name}@{name}&{self.baseline}' def parse_dataset_key(dataset_key): """Parse dataset key to extract dataset_name, subset_name, and model pair.""" parts = dataset_key.split('@') dataset_subset = parts[0] model_pair = parts[1] dataset_name, subset_name = dataset_subset.split('&', 1) model_1, model_2 = model_pair.split('&', 1) return dataset_name, subset_name, model_1, model_2 # Add parsed columns parsed_data = [] for _, row in winrate_df.iterrows(): dataset_name, subset_name, model_1, model_2 = parse_dataset_key(row[ReportKey.subset_name]) if dataset_name is not None: parsed_data.append({ 'dataset_name': dataset_name, 'subset_name': subset_name, ReportKey.model_name: model_1, ReportKey.metric_name: row[ReportKey.metric_name], ReportKey.score: row[ReportKey.score] }) if not parsed_data: logger.warning('No valid dataset keys found for parsing.') return parsed_df = pd.DataFrame(parsed_data) # 1. Overall ranking (aggregate across all datasets and subsets) overall_df = parsed_df.groupby([ReportKey.model_name, ReportKey.metric_name])[ReportKey.score].mean().reset_index() leaderboard_outputs.append(format_leaderboard(overall_df, '=== OVERALL LEADERBOARD ===')) # 2. Dataset-level rankings datasets = parsed_df['dataset_name'].unique() for dataset in sorted(datasets): dataset_df = parsed_df[parsed_df['dataset_name'] == dataset] dataset_agg = dataset_df.groupby([ReportKey.model_name, ReportKey.metric_name])[ReportKey.score].mean().reset_index() leaderboard_outputs.append(format_leaderboard(dataset_agg, f'=== DATASET LEADERBOARD: {dataset} ===')) # 3. Subset-level rankings subsets = parsed_df[['dataset_name', 'subset_name']].drop_duplicates() for _, subset_row in subsets.iterrows(): dataset_name = subset_row['dataset_name'] subset_name = subset_row['subset_name'] subset_df = parsed_df[(parsed_df['dataset_name'] == dataset_name) & (parsed_df['subset_name'] == subset_name)] leaderboard_outputs.append( format_leaderboard(subset_df, f'=== SUBSET LEADERBOARD: {dataset_name} - {subset_name} ===')) # Write all leaderboard outputs to file with open(leaderboard_file, 'w', encoding='utf-8') as f: f.write('\n'.join(leaderboard_outputs)) logger.info(f'Leaderboard results saved to: {leaderboard_file}') def get_gold_answer(self, input_d): return f"model_1: {input_d['model_1']}\n---\n" + input_d['answer_1'] def llm_parse_pred_result(self, result, raw_input_d=None, eval_type=EvalType.CHECKPOINT): return f"model_2: {raw_input_d['model_2']}\n---\n" + raw_input_d['answer_2'] def match(self, gold, pred): logger.warning(f'Please use LLMJudge to match the result for {self.name}') return