Add mt-bench

extended_tasks/mt_bench/judge_prompts.jsonl

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+{"name": "pair-v2", "type": "pairwise", "system_prompt": "Please act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question displayed below. You should choose the assistant that follows the user's instructions and answers the user's question better. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of their responses. Begin your evaluation by comparing the two responses and provide a short explanation. Avoid any position biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. After providing your explanation, output your final verdict by strictly following this format: \"[[A]]\" if assistant A is better, \"[[B]]\" if assistant B is better, and \"[[C]]\" for a tie.", "prompt_template": "[User Question]\n{question}\n\n[The Start of Assistant A's Answer]\n{answer_a}\n[The End of Assistant A's Answer]\n\n[The Start of Assistant B's Answer]\n{answer_b}\n[The End of Assistant B's Answer]", "description": "Prompt for general questions", "category": "general", "output_format": "[[A]]"}
+{"name": "pair-v2-multi-turn", "type": "pairwise", "system_prompt": "Please act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user questions. You should choose the assistant that follows the user's instructions and answers the user's questions better. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of their responses. You should focus on who provides a better answer to the second user question. Begin your evaluation by comparing the responses of the two assistants and provide a short explanation. Avoid any position biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. After providing your explanation, output your final verdict by strictly following this format: \"[[A]]\" if assistant A is better, \"[[B]]\" if assistant B is better, and \"[[C]]\" for a tie.", "prompt_template": "<|The Start of Assistant A's Conversation with User|>\n\n### User:\n{question_1}\n\n### Assistant A:\n{answer_a_1}\n\n### User:\n{question_2}\n\n### Assistant A:\n{answer_a_2}\n\n<|The End of Assistant A's Conversation with User|>\n\n\n<|The Start of Assistant B's Conversation with User|>\n\n### User:\n{question_1}\n\n### Assistant B:\n{answer_b_1}\n\n### User:\n{question_2}\n\n### Assistant B:\n{answer_b_2}\n\n<|The End of Assistant B's Conversation with User|>", "description": "Prompt for multi-turn general questions", "category": "general", "output_format": "[[A]]"}
+{"name": "pair-math-v1", "type": "pairwise", "system_prompt": "Please act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question displayed below. Your evaluation should consider correctness and helpfulness. You will be given a reference answer, assistant A's answer, and assistant B's answer. Your job is to evaluate which assistant's answer is better. Begin your evaluation by comparing both assistants' answers with the reference answer. Identify and correct any mistakes. Avoid any position biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. After providing your explanation, output your final verdict by strictly following this format: \"[[A]]\" if assistant A is better, \"[[B]]\" if assistant B is better, and \"[[C]]\" for a tie.", "prompt_template": "[User Question]\n{question}\n\n[The Start of Reference Answer]\n{ref_answer_1}\n[The End of Reference Answer]\n\n[The Start of Assistant A's Answer]\n{answer_a}\n[The End of Assistant A's Answer]\n\n[The Start of Assistant B's Answer]\n{answer_b}\n[The End of Assistant B's Answer]", "description": "Prompt for math questions", "category": "math", "output_format": "[[A]]"}
+{"name": "pair-math-v1-multi-turn", "type": "pairwise", "system_prompt": "Please act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user questions. Your evaluation should consider correctness and helpfulness. You will be given reference answers, the assistant A's answers, the assistant B's answers. Your job is to determine which assistant provides correct and helpful answers to the second user question. Begin your evaluation by comparing both assistants' answers with the reference answers. Identify and correct any mistakes. Avoid any position biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. After providing your explanation, output your final verdict by strictly following this format: \"[[A]]\" if assistant A is better, \"[[B]]\" if assistant B is better, and \"[[C]]\" for a tie.", "prompt_template": "<|The Start of Reference Answer|>\n\n### User:\n{question_1}\n\n### Reference answer:\n{ref_answer_1}\n\n### User:\n{question_2}\n\n### Reference answer:\n{ref_answer_2}\n\n<|The End of Reference Answer|>\n\n\n<|The Start of Assistant A's Conversation with User|>\n\n### User:\n{question_1}\n\n### Assistant A:\n{answer_a_1}\n\n### User:\n{question_2}\n\n### Assistant A:\n{answer_a_2}\n\n<|The End of Assistant A's Conversation with User|>\n\n\n<|The Start of Assistant B's Conversation with User|>\n\n### User:\n{question_1}\n\n### Assistant B:\n{answer_b_1}\n\n### User:\n{question_2}\n\n### Assistant B:\n{answer_b_2}\n\n<|The End of Assistant B's Conversation with User|>", "description": "Prompt for multi-turn general questions", "category": "general", "output_format": "[[A]]"}
+{"name": "single-v1", "type": "single", "system_prompt": "You are a helpful assistant.", "prompt_template": "[Instruction]\nPlease act as an impartial judge and evaluate the quality of the response provided by an AI assistant to the user question displayed below. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of the response. Begin your evaluation by providing a short explanation. Be as objective as possible. After providing your explanation, you must rate the response on a scale of 1 to 10 by strictly following this format: \"[[rating]]\", for example: \"Rating: [[5]]\".\n\n[Question]\n{question}\n\n[The Start of Assistant's Answer]\n{answer}\n[The End of Assistant's Answer]", "description": "Prompt for general questions", "category": "general", "output_format": "[[rating]]"}
+{"name": "single-math-v1", "type": "single", "system_prompt": "You are a helpful assistant.", "prompt_template": "[Instruction]\nPlease act as an impartial judge and evaluate the quality of the response provided by an AI assistant to the user question displayed below. Your evaluation should consider correctness and helpfulness. You will be given a reference answer and the assistant's answer. Begin your evaluation by comparing the assistant's answer with the reference answer. Identify and correct any mistakes. Be as objective as possible. After providing your explanation, you must rate the response on a scale of 1 to 10 by strictly following this format: \"[[rating]]\", for example: \"Rating: [[5]]\".\n\n[Question]\n{question}\n\n[The Start of Reference Answer]\n{ref_answer_1}\n[The End of Reference Answer]\n\n[The Start of Assistant's Answer]\n{answer}\n[The End of Assistant's Answer]", "description": "Prompt for general questions", "category": "math", "output_format": "[[rating]]"}
+{"name": "single-v1-multi-turn", "type": "single", "system_prompt": "Please act as an impartial judge and evaluate the quality of the response provided by an AI assistant to the user question displayed below. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of the response. You evaluation should focus on the assistant's answer to the second user question. Begin your evaluation by providing a short explanation. Be as objective as possible. After providing your explanation, you must rate the response on a scale of 1 to 10 by strictly following this format: \"[[rating]]\", for example: \"Rating: [[5]]\".\n\n", "prompt_template": "<|The Start of Assistant A's Conversation with User|>\n\n### User:\n{question_1}\n\n### Assistant A:\n{answer_1}\n\n### User:\n{question_2}\n\n### Assistant A:\n{answer_2}\n\n<|The End of Assistant A's Conversation with User|>", "description": "Prompt for general questions", "category": "general", "output_format": "[[rating]]"}
+{"name": "single-math-v1-multi-turn", "type": "single", "system_prompt": "Please act as an impartial judge and evaluate the quality of the response provided by an AI assistant to the user question. Your evaluation should consider correctness and helpfulness. You will be given a reference answer and the assistant's answer. You evaluation should focus on the assistant's answer to the second question. Begin your evaluation by comparing the assistant's answer with the reference answer. Identify and correct any mistakes. Be as objective as possible. After providing your explanation, you must rate the response on a scale of 1 to 10 by strictly following this format: \"[[rating]]\", for example: \"Rating: [[5]]\".\n\n", "prompt_template": "<|The Start of Reference Answer|>\n\n### User:\n{question_1}\n\n### Reference answer:\n{ref_answer_1}\n\n### User:\n{question_2}\n\n### Reference answer:\n{ref_answer_2}\n\n<|The End of Reference Answer|>\n\n\n<|The Start of Assistant A's Conversation with User|>\n\n### User:\n{question_1}\n\n### Assistant A:\n{answer_1}\n\n### User:\n{question_2}\n\n### Assistant A:\n{answer_2}\n\n<|The End of Assistant A's Conversation with User|>", "description": "Prompt for general questions", "category": "math", "output_format": "[[rating]]"}

extended_tasks/mt_bench/judges.py

@@ -0,0 +1,136 @@
+# MIT License
+
+# Copyright (c) 2024 The HuggingFace Team
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+
+import ast
+import json
+import re
+import time
+from abc import ABC
+from typing import Optional
+
+from openai import OpenAI
+
+from lighteval.logging.hierarchical_logger import hlog_warn
+
+
+# Abstract class for a judge
+class Judge(ABC):
+    def evaluate_answer(answers, questions, references) -> tuple[str, list[dict[str, str]], str]:
+        pass
+
+
+class JudgeOpenAI(Judge):
+    def __init__(self, model: str, seed: int, temperature: float, templates_path: str):
+        self.client = OpenAI()
+        self.model = model
+        self.seed = seed
+        self.temperature = temperature
+
+        data = []
+        with open(templates_path, "r") as f:
+            for line in f:
+                tmp = json.loads(line)
+                data.append(tmp)
+
+        self.templates = {d["name"]: d for d in data}
+
+        self.one_score_pattern = re.compile(r"\[\[(\d+\.?\d*)\]\]")
+        self.one_score_pattern_backup = re.compile(r"\[(\d+\.?\d*)\]")
+
+        self.API_MAX_RETRY = 16
+        self.API_RETRY_SLEEP = 10
+        self.max_tokens = 2048
+
+    def evaluate_answer(
+        self, questions: list[str], answers: list[str], references: list[str], single_turn: bool
+    ) -> tuple[int, list[dict[str, str]], str]:
+        if single_turn:
+            prompts = self.__get_prompts_single_turn(
+                questions[0], answers[0], references[0] if len(references) > 0 else None
+            )
+        else:
+            prompts = self.__get_prompts_multi_turn(questions, answers, references if len(references) > 0 else None)
+
+        for _ in range(self.API_MAX_RETRY):
+            try:
+                response = self.client.chat.completions.create(
+                    model=self.model,
+                    seed=self.seed,
+                    temperature=self.temperature,
+                    messages=prompts,
+                    max_tokens=self.max_tokens,
+                    n=1,
+                )
+                break
+            except Exception as e:
+                hlog_warn(f"{type(e), e}")
+                time.sleep(self.API_RETRY_SLEEP)
+
+        judgment = response.choices[0].message.content
+        score = self.__process_judge_response(judgment)
+
+        return score, prompts, judgment
+
+    def __get_prompts_multi_turn(
+        self, questions: list[str], answers: list[str], references: Optional[list[str]]
+    ) -> list[dict[str, str]]:
+        if references is None:
+            system_prompt = {"role": "system", "content": self.templates["single-v1-multi-turn"]["system_prompt"]}
+            user_prompt_str = self.templates["single-v1-multi-turn"]["prompt_template"].format(
+                question_1=questions[0], answer_1=answers[0], question_2=questions[1], answer_2=answers[1]
+            )
+        else:
+            system_prompt = {"role": "system", "content": self.templates["single-math-v1-multi-turn"]["system_prompt"]}
+            user_prompt_str = self.templates["single-math-v1-multi-turn"]["prompt_template"].format(
+                question_1=questions[0],
+                answer_1=answers[0],
+                ref_answer_1=references[0],
+                question_2=questions[1],
+                answer_2=answers[1],
+                ref_answer_2=references[1],
+            )
+        user_prompt = {"role": "user", "content": user_prompt_str}
+        return [system_prompt, user_prompt]
+
+    def __get_prompts_single_turn(self, question: str, answer: str, reference: Optional[str]) -> list[dict[str, str]]:
+        if reference is None:
+            system_prompt = {"role": "system", "content": self.templates["single-v1"]["system_prompt"]}
+            user_prompt_str = self.templates["single-v1"]["prompt_template"].format(question=question, answer=answer)
+        else:
+            system_prompt = {"role": "system", "content": self.templates["single-math-v1"]["system_prompt"]}
+            user_prompt_str = self.templates["single-math-v1"]["prompt_template"].format(
+                question=question, answer=answer, ref_answer_1=reference
+            )
+        user_prompt = {"role": "user", "content": user_prompt_str}
+        return [system_prompt, user_prompt]
+
+    def __process_judge_response(self, judgment: str) -> int:
+        match = re.search(self.one_score_pattern, judgment)
+        if not match:
+            match = re.search(self.one_score_pattern_backup, judgment)
+        if match:
+            rating = ast.literal_eval(match.groups()[0])
+        else:
+            rating = -1
+
+        return rating