自我一致性

def self_consistency(prompt, model, num_samples=5, temperature=0.7):
    """
    实现自我一致性推理
    """
    answers = []
    
    # 1. 生成多条推理路径
    for _ in range(num_samples):
        response = model.generate(
            prompt + "\n让我们一步步思考：",
            temperature=temperature
        )
        # 2. 提取答案
        answer = extract_answer(response)
        answers.append(answer)
    
    # 3. 多数投票
    from collections import Counter
    vote_counts = Counter(answers)
    final_answer = vote_counts.most_common(1)[0][0]
    confidence = vote_counts[final_answer] / num_samples
    
    return final_answer, confidence

def dynamic_self_consistency(prompt, model):
    """
    动态调整采样数量的自我一致性
    """
    answers = []
    confidence_scores = []
    
    for i in range(1, max_samples):
        # 生成新的推理路径
        response = model.generate(prompt, temperature=0.7)
        answer = extract_answer(response)
        
        # 计算置信度（7个文本指标）
        confidence = calculate_confidence(response)
        
        answers.append(answer)
        confidence_scores.append(confidence)
        
        # 早停检查：如果答案一致性足够高
        if check_convergence(answers, confidence_scores):
            break
    
    # 加权投票而非简单多数投票
    return weighted_vote(answers, confidence_scores)

def universal_self_consistency(prompt, model, task_type="generation"):
    """
    适用于摘要、创意写作等开放式任务
    """
    outputs = []
    
    # 生成多个输出
    for _ in range(5):
        output = model.generate(prompt, temperature=0.8)
        outputs.append(output)
    
    # 使用LLM判断一致性
    consistency_prompt = f"""
    以下是对同一问题的多个回答：
    {format_outputs(outputs)}
    
    请识别最一致和高质量的回答。
    """
    
    best_output = model.select_best(consistency_prompt)
    return best_output