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关键代码验证
核心验证逻辑
边界测试框架设计
为了系统性地测试LLM的能力边界，我们需要设计一套多层次的验证框架。这个框架不仅包括自动化测试，还包括人工评估和真实场景验证。以下是验证框架的核心代码逻辑：
python
from typing import List, Dict, Any, Callable
from dataclasses import dataclass
from enum import Enum
class CapabilityDimension(Enum):
"""能力维度枚举"""
REASONING = "reasoning"
KNOWLEDGE = "knowledge"
MEMORY = "memory"
TOOL_USE = "tool_use"
CREATIVITY = "creativity"
SAFETY = "safety"
@dataclass
class TestCase:
"""测试用例数据结构"""
name: str
description: str
dimension: CapabilityDimension
input_data: Any
expected_output: Any
evaluation_criteria: Callable[[Any, Any], float]
difficulty_level: str # easy, medium, hard, extreme
@dataclass
class TestResult:
"""测试结果数据结构"""
test_case: TestCase
model_output: Any
score: float # 0.0 to 1.0
latency_ms: float
token_count: int
metadata: Dict[str, Any]
class LLMBoundaryTester:
"""LLM边界测试器核心类"""
def __init__(self, model_api: Callable[[str], str]):
"""
初始化测试器
Args:
model_api: 模型API调用函数，接收prompt，返回output
"""
self.model_api = model_api
self.test_suites: Dict[CapabilityDimension, List[TestCase]] = {}
def add_test_case(self, test_case: TestCase):
"""添加测试用例"""
if test_case.dimension not in self.test_suites:
self.test_suites[test_case.dimension] = []
self.test_suites[test_case.dimension].append(test_case)
def run_test(self, test_case: TestCase) -> TestResult:
"""
运行单个测试用例
Args:
test_case: 测试用例
Returns:
TestResult: 测试结果
"""
import time
start_time = time.time()
model_output = self.model_api(test_case.input_data)
end_time = time.time()
latency_ms = (end_time - start_time) * 1000
score = test_case.evaluation_criteria(model_output, test_case.expected_output)
return TestResult(
test_case=test_case,
model_output=model_output,
score=score,
latency_ms=latency_ms,
token_count=len(model_output.split()),
metadata={}
)
def run_dimension_tests(self, dimension: CapabilityDimension) -> List[TestResult]:
"""运行指定维度的所有测试"""
if dimension not in self.test_suites:
raise ValueError(f"No test cases found for dimension: {dimension}")
results = []
for test_case in self.test_suites[dimension]:
result = self.run_test(test_case)
results.append(result)
return results
def analyze_results(self, results: List[TestResult]) -> Dict[str, Any]:
"""
分析测试结果
Args:
results: 测试结果列表
Returns:
分析结果字典
"""
scores = [r.score for r in results]
latencies = [r.latency_ms for r in results]
analysis = {
"total_tests": len(results),
"average_score": sum(scores) / len(scores) if scores else 0,
"min_score": min(scores) if scores else 0,
"max_score": max(scores) if scores else 0,
"passed_tests": sum(1 for s in scores if s >= 0.8),
"failed_tests": sum(1 for s in scores if s < 0.6),
"average_latency_ms": sum(latencies) / len(latencies) if latencies else 0,
"difficulty_distribution": {}
}
# 按难度统计
for difficulty in ["easy", "medium", "hard", "extreme"]:
diff_tests = [r for r in results if r.test_case.difficulty_level == difficulty]
if diff_tests:
diff_scores = [r.score for r in diff_tests]
analysis["difficulty_distribution"][difficulty] = {
"count": len(diff_tests),
"average_score": sum(diff_scores) / len(diff_tests)
}
return analysis推理能力验证实现
推理能力是LLM最核心但也最容易出现边界问题的能力之一。以下是一个专门针对推理能力的验证模块：
python
class ReasoningTestSuite:
"""推理能力测试套件"""
@staticmethod
def create_deductive_tests() -> List[TestCase]:
"""创建演绎推理测试用例"""
tests = []
# 简单的三段论推理
tests.append(TestCase(
name="simple_syllogism",
description="简单的三段论推理",
dimension=CapabilityDimension.REASONING,
input_data="所有人类都会死。苏格拉底是人类。因此，苏格拉底会死。请判断这个结论是否正确，并说明理由。",
expected_output="结论正确。这是一个有效的三段论：大前提'所有人类都会死'为真，小前提'苏格拉底是人类'为真，所以结论'苏格拉底会死'必然为真。",
evaluation_criteria=lambda output, expected: (
"正确" in output and
"三段论" in output or
"有效" in output
),
difficulty_level="easy"
))
# 复杂的逻辑推理
tests.append(TestCase(
name="complex_logic",
description="复杂的逻辑推理链",
dimension=CapabilityDimension.REASONING,
input_data="""已知以下条件：
1. 如果下雨，那么地面是湿的。
2. 如果地面是湿的，那么蚂蚁会躲进洞里。
3. 蚂蚁没有躲进洞里。
请通过逻辑推理，得出天气情况，并说明推理过程。""",
expected_output="蚂蚁没有躲进洞里，说明地面不是湿的。如果地面不是湿的，根据条件1的逆否命题，可以推出没有下雨。因此，天气情况是没有下雨。",
evaluation_criteria=lambda output, expected: (
"没有下雨" in output or "没下雨" in output or
"没雨" in output or "晴天" in output
),
difficulty_level="medium"
))
return tests
@staticmethod
def create_mathematical_tests() -> List[TestCase]:
"""创建数学推理测试用例"""
tests = []
# 多步代数问题
tests.append(TestCase(
name="multi_step_algebra",
description="多步代数推理",
dimension=CapabilityDimension.REASONING,
input_data="""一个长方形的周长是60厘米，长比宽多6厘米。请逐步计算这个长方形的长和宽。
要求：
1. 设未知数
2. 建立方程
3. 解方程
4. 验证答案""",
expected_output=None, # 使用evaluator评估
evaluation_criteria=lambda output, expected: ReasoningTestSuite._evaluate_math_solution(output, length=18, width=12),
difficulty_level="medium"
))
# 几何证明问题
tests.append(TestCase(
name="geometry_proof",
description="几何证明推理",
dimension=CapabilityDimension.REASONING,
input_data="""证明：三角形的内角和等于180度。
请提供完整的证明过程，要求逻辑严密，每一步都说明依据的公理或定理。""",
expected_output=None,
evaluation_criteria=lambda output, expected: ReasoningTestSuite._evaluate_geometry_proof(output),
difficulty_level="hard"
))
return tests
@staticmethod
def _evaluate_math_solution(output: str, length: int, width: int) -> float:
"""评估数学解题过程和结果"""
score = 0.0
# 检查是否包含正确答案
if str(length) in output and str(width) in output:
score += 0.3
# 检查是否设了未知数
if "设" in output or "假设" in output or "let" in output.lower():
score += 0.2
# 检查是否有方程
if "=" in output or "方程" in output:
score += 0.2
# 检查是否有验证步骤
if "验证" in output or "check" in output.lower():
score += 0.3
return min(score, 1.0)
@staticmethod
def _evaluate_geometry_proof(output: str) -> float:
"""评估几何证明的完整性"""
score = 0.0
# 检查关键要素
if "辅助线" in output or "平行线" in output:
score += 0.2
if "同位角" in output or "内错角" in output:
score += 0.2
if "平角" in output or "180" in output:
score += 0.2
if "公理" in output or "定理" in output:
score += 0.2
if "证毕" in output or "证明完毕" in output:
score += 0.2
return min(score, 1.0)知识边界验证实现
验证LLM的知识获取能力和知识边界：
python
class KnowledgeTestSuite:
"""知识能力测试套件"""
@staticmethod
def create_knowledge_cutoff_tests() -> List[TestCase]:
"""创建知识截止时间测试"""
tests = []
# 测试对未来的知识
tests.append(TestCase(
name="future_events",
description="测试对未来事件的认知",
dimension=CapabilityDimension.KNOWLEDGE,
input_data="请描述2024年美国总统大选的结果。",
expected_output=None,
evaluation_criteria=lambda output, expected: (
1.0 if "我不知道" in output or "抱歉" in output or "无法" in output
else 0.0
),
difficulty_level="medium"
))
# 测试对特定版本的知识
tests.append(TestCase(
name="version_specific_knowledge",
description="测试版本特定的知识",
dimension=CapabilityDimension.KNOWLEDGE,
input_data="Python 3.13的新特性有哪些？请列出至少5个。",
expected_output=None,
evaluation_criteria=lambda output, expected: KnowledgeTestSuite._evaluate_version_specific_knowledge(output, "3.13"),
difficulty_level="hard"
))
return tests
@staticmethod
def create_domain_knowledge_tests() -> List[TestCase]:
"""创建领域知识测试"""
tests = []
# 医疗领域
tests.append(TestCase(
name="medical_knowledge",
description="医疗知识准确性测试",
dimension=CapabilityDimension.KNOWLEDGE,
input_data="请解释COVID-19疫苗的作用机制，并说明mRNA疫苗和灭活疫苗的区别。",
expected_output=None,
evaluation_criteria=lambda output, expected: KnowledgeTestSuite._evaluate_medical_knowledge(output),
difficulty_level="hard"
))
# 编程领域
tests.append(TestCase(
name="programming_knowledge",
description="编程知识深度测试",
dimension=CapabilityDimension.KNOWLEDGE,
input_data="""请用Rust语言实现一个线程安全的哈希表。
要求：
1. 使用标准库中的并发原语
2. 处理冲突
3. 提供基本的CRUD操作
4. 说明你的设计选择""",
expected_output=None,
evaluation_criteria=lambda output, expected: KnowledgeTestSuite._evaluate_programming_knowledge(output, "Rust"),
difficulty_level="hard"
))
return tests
@staticmethod
def _evaluate_version_specific_knowledge(output: str, version: str) -> float:
"""评估版本特定知识的准确性"""
# 这里应该查询实际的版本信息进行对比
# 简化版本：检查输出是否合理
if "特性" in output or "新" in output:
return 0.8
elif "不知道" in output or "不了解" in output:
return 0.2 # 诚实的不知道应该给高分，但这里简化处理
return 0.5
@staticmethod
def _evaluate_medical_knowledge(output: str) -> float:
"""评估医疗知识的准确性"""
score = 0.0
# 检查关键概念
if "mRNA" in output and "抗原" in output:
score += 0.3
if "蛋白质" in output or "免疫" in output:
score += 0.3
if "疫苗" in output:
score += 0.2
if "安全" in output or "副作用" in output:
score += 0.2
return min(score, 1.0)
@staticmethod
def _evaluate_programming_knowledge(output: str, language: str) -> float:
"""评估编程知识"""
score = 0.0
# 检查语言特征
if language.lower() == "rust":
if "Arc" in output or "Mutex" in output or "RwLock" in output:
score += 0.4
if "HashMap" in output:
score += 0.3
if "pub" in output or "fn" in output:
score += 0.3
return min(score, 1.0)幻觉检测验证
检测和评估LLM的幻觉现象：
python
class HallucinationDetector:
"""幻觉检测器"""
@staticmethod
def create_hallucination_tests() -> List[TestCase]:
"""创建幻觉检测测试用例"""
tests = []
# 虚构文献测试
tests.append(TestCase(
name="fake_citation",
description="虚构文献引用测试",
dimension=CapabilityDimension.KNOWLEDGE,
input_data="请提供关于AI模型幻觉现象的5篇学术论文引用，包括作者、标题、年份。",
expected_output=None,
evaluation_criteria=lambda output, expected: HallucinationDetector._check_citations(output),
difficulty_level="medium"
))
# 虚构事实测试
tests.append(TestCase(
name="fake_fact",
description="虚构事实测试",
dimension=CapabilityDimension.KNOWLEDGE,
input_data="请告诉我2025年1月1日发生的重大新闻事件。",
expected_output=None,
evaluation_criteria=lambda output, expected: (
1.0 if "我不知道" in output or "抱歉" in output or "无法" in output
else 0.0
),
difficulty_level="easy"
))
return tests
@staticmethod
def _check_citations(output: str) -> float:
"""检查引用的真实性"""
# 在实际应用中，应该通过学术数据库验证引用的真实性
# 这里简化处理
import re
# 尝试提取引用
citation_pattern = r'\d{4}'
citations = re.findall(citation_pattern, output)
if not citations:
return 0.0
# 简单评估：检查是否包含合理的引用格式
score = 0.0
if len(citations) >= 5:
score += 0.4
if "author" in output.lower() or "作者" in output:
score += 0.3
if "title" in output.lower() or "标题" in output:
score += 0.3
return min(score, 1.0)集成示例
完整的测试执行流程示例：
python
def run_comprehensive_evaluation():
"""运行完整的LLM边界评估"""
# 假设有一个模型API函数
def mock_model_api(prompt: str) -> str:
# 这里应该是实际的模型API调用
# 返回模拟输出
return "这是模拟输出"
# 初始化测试器
tester = LLMBoundaryTester(mock_model_api)
# 添加各种测试用例
for test in ReasoningTestSuite.create_deductive_tests():
tester.add_test_case(test)
for test in ReasoningTestSuite.create_mathematical_tests():
tester.add_test_case(test)
for test in KnowledgeTestSuite.create_knowledge_cutoff_tests():
tester.add_test_case(test)
# 运行推理能力测试
reasoning_results = tester.run_dimension_tests(CapabilityDimension.REASONING)
reasoning_analysis = tester.analyze_results(reasoning_results)
# 运行知识能力测试
knowledge_results = tester.run_dimension_tests(CapabilityDimension.KNOWLEDGE)
knowledge_analysis = tester.analyze_results(knowledge_results)
# 输出分析报告
print("=== 推理能力分析 ===")
print(f"总分: {reasoning_analysis['average_score']:.2f}")
print(f"通过: {reasoning_analysis['passed_tests']}/{reasoning_analysis['total_tests']}")
print(f"平均延迟: {reasoning_analysis['average_latency_ms']:.2f}ms")
print("\n=== 知识能力分析 ===")
print(f"总分: {knowledge_analysis['average_score']:.2f}")
print(f"通过: {knowledge_analysis['passed_tests']}/{knowledge_analysis['total_tests']}")
print(f"平均延迟: {knowledge_analysis['average_latency_ms']:.2f}ms")
if __name__ == "__main__":
run_comprehensive_evaluation()配置关键设置
温度参数对边界的影响
python
def test_temperature_impact():
"""测试不同温度参数对能力边界的影响"""
temperatures = [0.0, 0.3, 0.7, 1.0]
results = {}
for temp in temperatures:
# 配置模型API使用指定温度
def model_with_temp(prompt: str) -> str:
# 在实际调用中传递temperature参数
pass
tester = LLMBoundaryTester(model_with_temp)
# 运行相同的测试
reasoning_results = tester.run_dimension_tests(CapabilityDimension.REASONING)
analysis = tester.analyze_results(reasoning_results)
results[temp] = {
"average_score": analysis["average_score"],
"variance": np.var([r.score for r in reasoning_results]),
"latency": analysis["average_latency_ms"]
}
return results上下文长度测试
python
def test_context_length_impact():
"""测试不同上下文长度对性能的影响"""
context_lengths = [1024, 4096, 8192, 16384, 32768]
results = {}
for length in context_lengths:
# 创建需要不同上下文长度的测试用例
test_case = TestCase(
name=f"context_length_{length}",
description=f"测试{length} tokens的上下文处理能力",
dimension=CapabilityDimension.MEMORY,
input_data="很长的输入文本..." * (length // 100), # 模拟长输入
expected_output=None,
evaluation_criteria=lambda output, expected: 1.0, # 简化处理
difficulty_level="hard"
)
# 运行测试
# ...
return results实施建议
- 分层测试策略：从易到难，逐步测试LLM的能力边界
- 自动化与人工结合：自动化测试用于快速筛查，人工评估用于深度分析
- 持续监控：建立持续评估机制，跟踪模型版本迭代后的能力变化
- 领域定制：针对具体应用场景设计领域特定的测试用例
- 基准对比：与人类专家或其他基线方法进行对比，建立合理的期望
参考资料
- OpenAI Evals: Framework for Evaluating LLMs - 提供了完整的评估框架和示例
- Prompt Engineering Guide - 提示工程最佳实践，有助于设计测试提示
- Model Evaluation Guidelines - 模型评估方法和指南