如何解决大模型生成内容的准确性问题？

import re

def retrieve(query, documents):
    '''
    检索函数 用于在给定文档列表中寻找与查询高度匹配的内容
    输入参数 query 为用户输入查询字符串，documents 为包含多个文档的列表
    输出为匹配度较高的文档列表
    '''
    results = []
    for doc in documents:
        if query.lower() in doc.lower():
            results.append(doc)
    return results

def generate_response(query, retrieved_docs):
    '''
    生成函数 模拟 GPT 模型的回答生成过程
    将用户查询与检索到的文档拼接组合，生成初步回答
    '''
    combined_info = ' '.join(retrieved_docs)
    response = f'Response to {query}: {combined_info}'
    return response

def post_process(response):
    '''
    后处理函数 对生成回答进行细节优化与格式修正
    包括去除冗余空格以及进行简单事实校验（示例用法）
    '''
    response = re.sub(r'\s+', ' ', response)
    if 'GPT' in response and '错误' in response:
        response = response.replace('错误', '正确')
    return response.strip()

if __name__ == '__main__':
    documents = [
        'This is a document about AI and machine learning. 此文档涉及 AI 与 机器学习 的基础知识。',
        'Deep learning and reinforcement learning are subsets of AI. 这份文档详细阐述了 深度学习 与 强化学习 的概念。',
        'GPT models are a breakthrough in natural language processing. 此文档描述了 GPT 模型 在 自然语言处理 方面 的突破性进展。'
    ]
    query = 'GPT models'
    retrieved = retrieve(query, documents)
    generated = generate_response(query, retrieved)
    final_output = post_process(generated)
    print(final_output)

import re
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer
from transformers import GPT2LMHeadModel, GPT2Tokenizer

def build_faiss_index(documents, model):
    '''
    构建 FAISS 索引时，需要将所有文档转换为向量，随后构建索引结构
    '''
    embeddings = model.encode(documents)
    dimension = embeddings.shape[1]
    index = faiss.IndexFlatL2(dimension)
    index.add(np.array(embeddings, dtype=np.float32))
    return index, embeddings

def faiss_retrieve(query, documents, index, model, top_k=3):
    '''
    利用 FAISS 索引对查询进行向量检索，返回 top_k 个最相似文档
    '''
    query_vec = model.encode([query])
    distances, indices = index.search(np.array(query_vec, dtype=np.float32), top_k)
    results = [documents[i] for i in indices[0]]
    return results

def generate_gpt_response(query, retrieved_docs, tokenizer, model):
    '''
    利用 GPT 模型生成回答，将用户查询与检索到的文档作为上下文输入
    '''
    context = ' '.join(retrieved_docs)
    input_text = f'Question: {query} Context: {context} Answer:'
    inputs = tokenizer.encode(input_text, return_tensors='pt')
    outputs = model.generate(inputs, max_length=200, num_return_sequences=1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

def advanced_post_process(response):
    '''
    高级后处理模块包括去除多余空格与简单事实校验
    '''
    response = re.sub(r'\s+', ' ', response)
    if 'inaccurate' in response:
        response = response.replace('inaccurate', 'accurate')
    return response.strip()

if __name__ == '__main__':
    documents = [
        'Artificial intelligence and machine learning are transforming industries.',
        'Deep learning has revolutionized computer vision and natural language processing.',
        'GPT models represent a breakthrough in generative AI and have numerous applications.'
    ]
    query = 'What are GPT models?'
    sent_model = SentenceTransformer('all-MiniLM-L6-v2')
    faiss_index, embeddings = build_faiss_index(documents, sent_model)
    retrieved_docs = faiss_retrieve(query, documents, faiss_index, sent_model)
    
    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
    gpt_model = GPT2LMHeadModel.from_pretrained('gpt2')
    
    raw_response = generate_gpt_response(query, retrieved_docs, tokenizer, gpt_model)
    final_response = advanced_post_process(raw_response)
    print(final_response)