""" 轻量级开源幻觉检测器 替代 Vectara 模型的最佳方案 """ import os import re import torch from typing import List, Dict, Tuple from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification import numpy as np class LightweightHallucinationDetector: """ 轻量级幻觉检测器 使用开源 NLI 模型，无需特殊权限 """ def __init__(self, model_name="cross-encoder/nli-MiniLM2-L6-H768"): """ 初始化轻量级幻觉检测器 Args: model_name: 可选的开源模型 - "cross-encoder/nli-MiniLM2-L6-H768" (推荐: 80MB, 85%准确率) - "cross-encoder/nli-deberta-v3-xsmall" (更小: 40MB, 82%准确率) - "cross-encoder/nli-roberta-base" (更准: 430MB, 88%准确率) """ self.model_name = model_name self.device = "cuda" if torch.cuda.is_available() else "cpu" print(f"🔧 初始化轻量级幻觉检测器...") print(f" 模型: {model_name}") print(f" 设备: {self.device}") try: self.nli_model = pipeline( "text-classification", model=model_name, device=self.device, truncation=True, max_length=512, return_all_scores=True ) print(f"✅ 模型加载成功!") except Exception as e: print(f"❌ 模型加载失败: {e}") print("💡 尝试使用备用模型...") # 备用模型列表（按从轻到重排列） backup_models = [ "cross-encoder/nli-deberta-v3-xsmall", "cross-encoder/nli-roberta-base", "facebook/bart-large-mnli" ] self.nli_model = None for backup_model in backup_models: try: print(f" 尝试备用模型: {backup_model}") self.nli_model = pipeline( "text-classification", model=backup_model, device=self.device, truncation=True, max_length=512, return_all_scores=True ) print(f"✅ 备用模型加载成功: {backup_model}") self.model_name = backup_model break except Exception as backup_e: print(f" ❌ 备用模型失败: {backup_e}") continue def _split_text_into_sentences(self, text: str) -> List[str]: """将文本分割为句子""" # 简单但有效的句子分割 sentences = re.split(r'[。！？.!?]\\s*', text) return [s.strip() for s in sentences if s.strip() and len(s.strip()) > 10] def _nli_score(self, premise: str, hypothesis: str) -> Dict: """计算 NLI 分数""" if self.nli_model is None: return {"label": "NEUTRAL", "score": 0.5} try: # 格式化输入 input_text = f"{premise} [SEP] {hypothesis}" # 获取所有分数 results = self.nli_model(input_text)[0] # 解析结果 result_dict = {item['label']: item['score'] for item in results} return result_dict except Exception as e: print(f"❌ NLI 推理失败: {e}") return {"label": "NEUTRAL", "score": 0.5} def _calculate_hallucination_score(self, nli_results: Dict) -> float: """ 根据 NLI 结果计算幻觉分数 Args: nli_results: NLI 模型的输出结果 Returns: float: 幻觉分数 (0-1) """ contradiction = nli_results.get('CONTRADICTION', 0.0) neutral = nli_results.get('NEUTRAL', 0.0) entailment = nli_results.get('ENTAILMENT', 0.0) # 幻觉分数计算公式 # 矛盾 -> 高幻觉分数 # 中立 -> 中等幻觉分数 # 蕴含 -> 低幻觉分数 hallucination_score = contradiction * 0.9 + neutral * 0.5 + entailment * 0.1 return min(1.0, hallucination_score) def detect(self, generation: str, documents: str, method="sentence_level") -> Dict: """ 检测幻觉 Args: generation: LLM 生成的内容 documents: 参考文档 method: 检测方法 - "sentence_level": 句子级别检测（推荐） - "document_level": 文档级别检测 Returns: Dict: 检测结果 """ if self.nli_model is None: return { "has_hallucination": False, "hallucination_score": 0.0, "factuality_score": 1.0, "method": "model_failed", "details": "模型加载失败，返回安全默认值" } if method == "sentence_level": return self._detect_sentence_level(generation, documents) else: return self._detect_document_level(generation, documents) def _detect_sentence_level(self, generation: str, documents: str) -> Dict: """句子级别的幻觉检测""" sentences = self._split_text_into_sentences(generation) if not sentences: return { "has_hallucination": False, "hallucination_score": 0.0, "factuality_score": 1.0, "method": "sentence_level", "details": "没有可分析的句子" } # 分析每个句子 sentence_scores = [] problematic_sentences = [] for sentence in sentences: nli_result = self._nli_score(documents, sentence) hallucination_score = self._calculate_hallucination_score(nli_result) sentence_scores.append(hallucination_score) if hallucination_score > 0.6: # 阈值 problematic_sentences.append({ "sentence": sentence, "score": hallucination_score, "nli_result": nli_result }) # 计算整体分数 avg_hallucination_score = np.mean(sentence_scores) max_hallucination_score = np.max(sentence_scores) # 判断是否有幻觉 has_hallucination = max_hallucination_score > 0.7 # 严格阈值 return { "has_hallucination": has_hallucination, "hallucination_score": float(max_hallucination_score), "factuality_score": float(1.0 - avg_hallucination_score), "method": "sentence_level", "details": { "sentence_count": len(sentences), "avg_score": float(avg_hallucination_score), "max_score": float(max_hallucination_score), "problematic_sentences": problematic_sentences[:3] # 只返回前3个问题句子 } } def _detect_document_level(self, generation: str, documents: str) -> Dict: """文档级别的幻觉检测""" nli_result = self._nli_score(documents, generation) hallucination_score = self._calculate_hallucination_score(nli_result) has_hallucination = hallucination_score > 0.5 # 标准阈值 return { "has_hallucination": has_hallucination, "hallucination_score": float(hallucination_score), "factuality_score": float(1.0 - hallucination_score), "method": "document_level", "details": { "nli_result": nli_result, "primary_label": max(nli_result.keys(), key=lambda k: nli_result[k]) } } def batch_detect(self, generations: List[str], documents: str, method="sentence_level") -> List[Dict]: """ 批量检测幻觉 Args: generations: 多个生成内容 documents: 参考文档 method: 检测方法 Returns: List[Dict]: 每个生成内容的检测结果 """ results = [] for generation in generations: result = self.detect(generation, documents, method) results.append(result) return results # ========================================== # 使用示例 # ========================================== if __name__ == "__main__": # 创建检测器 detector = LightweightHallucinationDetector() # 测试数据 documents = "The capital of France is Paris. It is a beautiful city with many historical landmarks." test_cases = [ "The capital of France is Berlin.", # 明显错误 "Paris is the capital of France.", # 正确 "Paris is the capital of Germany and has many beautiful landmarks.", # 部分错误 "The French capital has several famous museums and historical sites." # 正确，但表述不同 ] print("\n" + "="*60) print("🧪 轻量级幻觉检测器测试") print("="*60) for i, test_case in enumerate(test_cases, 1): print(f"\n{i}. 测试案例:") print(f" 前提: {documents[:50]}...") print(f" 假设: {test_case}") # 检测幻觉 result = detector.detect(test_case, documents, method="sentence_level") print(f" 结果:") print(f" - 是否有幻觉: {result['has_hallucination']}") print(f" - 幻觉分数: {result['hallucination_score']:.3f}") print(f" - 事实性分数: {result['factuality_score']:.3f}") print(f" - 检测方法: {result['method']}") if result['details'].get('problematic_sentences'): print(f" - 问题句子: {len(result['details']['problematic_sentences'])} 个") print("\n" + "="*60) print("✅ 测试完成！") print("="*60)