{"id":69010,"date":"2026-01-31T01:08:59","date_gmt":"2026-01-30T17:08:59","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/69010.html"},"modified":"2026-01-31T01:08:59","modified_gmt":"2026-01-30T17:08:59","slug":"%e4%b8%8d%e8%a6%81%e5%9c%a8%e6%97%a0%e6%95%88%e7%a4%be%e4%ba%a4%e4%b8%8a%e6%b5%aa%e8%b4%b9%e7%94%9f%e5%91%bd%ef%bc%8c%e8%ae%a9ai%e9%94%80%e5%94%ae%e6%9c%ba%e5%99%a8%e4%ba%ba%e5%b8%ae%e4%bd%a0%e5%ae%8c","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/69010.html","title":{"rendered":"\u4e0d\u8981\u5728\u65e0\u6548\u793e\u4ea4\u4e0a\u6d6a\u8d39\u751f\u547d\uff0c\u8ba9AI\u9500\u552e\u673a\u5668\u4eba\u5e2e\u4f60\u5b8c\u6210\u524d\u671f\u6240\u6709\u201c\u7834\u51b0\u4e0e\u7b5b\u9009\u201d"},"content":{"rendered":"

\u4e00\u3001\u9500\u552e\u573a\u666f\u7684\u6838\u5fc3\u75db\u70b9：\u88ab\u65e0\u6548\u793e\u4ea4\u541e\u566c\u7684\u4eba\u529b\u6210\u672c<\/h3>\n

\u4f5c\u4e3aAI\u843d\u5730\u9886\u57df\u7684\u67b6\u6784\u5e08，\u6211\u89c1\u8bc1\u8fc7\u592a\u591aB2B\/B2C\u9500\u552e\u56e2\u961f\u7684\u56f0\u5883：2024\u5e74IDC\u5168\u7403\u667a\u80fd\u9500\u552e\u81ea\u52a8\u5316\u62a5\u544a\u663e\u793a，B2B\u9500\u552e\u524d\u671f\u7834\u51b0\u73af\u8282\u7684\u65e0\u6548\u6c9f\u901a\u5360\u6bd4\u8d8565%\u2014\u2014\u9500\u552e\u65e5\u5747\u62e8\u6253120\u4e2a\u7535\u8bdd，\u4ec530%\u80fd\u89e6\u8fbe\u6709\u6548\u7528\u6237，\u5176\u4e2d\u53c8\u670970%\u56e0\u9700\u6c42\u4e0d\u5339\u914d\u3001\u7528\u6237\u987e\u8651\u672a\u88ab\u7cbe\u51c6\u8bc6\u522b\u800c\u7ec8\u6b62\u3002\u4f20\u7edf\u4eba\u529b\u9500\u552e\u4e0d\u4ec5\u6548\u7387\u4f4e\u4e0b，\u8fd8\u8ba9\u6838\u5fc3\u9500\u552e\u8d44\u6e90\u6d88\u8017\u5728\u91cd\u590d\u7684\u201c\u7834\u51b0\u8bd5\u63a2\u201d\u4e2d\u3002<\/p>\n

\u9762\u5bf9\u8fd9\u4e00\u95ee\u9898，\u5927\u6a21\u578b\u9a71\u52a8\u7684AI\u9500\u552e\u673a\u5668\u4eba\u6210\u4e3a\u7834\u5c40\u5173\u952e：\u5b83\u80fd\u50cf\u201c\u61c2\u4f60\u201d\u7684\u4f19\u4f34\u4e00\u6837，\u7cbe\u51c6\u8bc6\u522b\u7528\u6237\u9700\u6c42\u3001\u7b5b\u9009\u6709\u6548\u7ebf\u7d22，\u5c06\u9500\u552e\u4ece\u65e0\u6548\u793e\u4ea4\u4e2d\u89e3\u653e\u51fa\u6765\u3002\u8fd9\u80cc\u540e\u7684\u6838\u5fc3\u652f\u6491\u662fNLP\u843d\u5730\u6280\u672f\u7684\u6210\u719f\u2014\u2014\u4ece\u610f\u56fe\u8bc6\u522b\u5230\u591a\u8f6e\u5bf9\u8bdd\u7ba1\u7406，\u4e00\u7cfb\u5217\u5de5\u7a0b\u5316\u4f18\u5316\u8ba9AI\u5177\u5907\u4e86\u7c7b\u4eba\u7684\u6c9f\u901a\u5224\u65ad\u529b\u3002<\/p>\n

\u4e8c\u3001AI\u9500\u552e\u673a\u5668\u4eba\u7684\u6838\u5fc3\u6280\u672f\u539f\u7406\u62c6\u89e3<\/h3>\n

2.1 \u610f\u56fe\u8bc6\u522b：\u7cbe\u51c6\u6355\u6349\u7528\u6237\u9700\u6c42\u7684\u6838\u5fc3\u6a21\u5757<\/h4>\n

\u610f\u56fe\u8bc6\u522bF1\u503c（\u9996\u6b21\u89e3\u91ca：\u8861\u91cf\u6a21\u578b\u5bf9\u7528\u6237\u9700\u6c42\u5206\u7c7b\u7684\u7cbe\u51c6\u5ea6，\u8303\u56f40-1，\u503c\u8d8a\u9ad8\u6a21\u578b\u5206\u7c7b\u9519\u8bef\u7387\u8d8a\u4f4e）\u662fAI\u9500\u552e\u673a\u5668\u4eba\u7684\u6838\u5fc3\u6307\u6807\u3002\u4f20\u7edf\u89c4\u5219\u5f15\u64ce\u4f9d\u8d56\u4eba\u5de5\u7f16\u5199\u5173\u952e\u8bcd\u89c4\u5219，\u9762\u5bf9\u53e3\u8bed\u5316\u3001\u65b9\u8a00\u5316\u7684\u7528\u6237\u8868\u8fbe（\u5982\u201c\u4f60\u4eec\u8fd9\u4ea7\u54c1\u80fd\u7ed9\u4e2d\u5c0f\u5382\u7528\u4e0d？\u201d）\u51c6\u786e\u7387\u4e0d\u8db370%\u3002<\/p>\n

2023\u5e74IEEE Transactions on Neural Networks and Learning Systems\u53d1\u8868\u7684\u300aFew-Shot Intent Detection for Low-Resource Customer Service Dialogues\u300b\u8bba\u6587\u6307\u51fa：\u57fa\u4e8e\u5927\u6a21\u578b\u7684\u5c11\u6837\u672c\u5fae\u8c03\u6280\u672f，\u4ec5\u9700100-500\u6761\u9500\u552e\u573a\u666f\u5bf9\u8bdd\u6570\u636e，\u5c31\u80fd\u5c06\u610f\u56fe\u8bc6\u522bF1\u503c\u63d0\u5347\u81f30.9\u4ee5\u4e0a\u3002\u5176\u539f\u7406\u662f\u5229\u7528\u5927\u6a21\u578b\u9884\u8bad\u7ec3\u7684\u901a\u7528\u8bed\u8a00\u7406\u89e3\u80fd\u529b，\u901a\u8fc7\u5c11\u91cf\u884c\u4e1a\u6837\u672c\u5feb\u901f\u9002\u914d\u9500\u552e\u573a\u666f\u7684\u9700\u6c42\u5206\u7c7b（\u5982\u201c\u9700\u6c42\u54a8\u8be2\u201d\u201c\u4ef7\u683c\u5f02\u8bae\u201d\u201c\u62d2\u7edd\u6c9f\u901a\u201d\u201c\u610f\u5411\u660e\u786e\u201d\u56db\u5927\u7c7b）\u3002<\/p>\n

2.2 \u591a\u8f6e\u5bf9\u8bdd\u72b6\u6001\u7ba1\u7406：\u8ffd\u8e2a\u7528\u6237\u9700\u6c42\u7684\u8bb0\u5fc6\u6a21\u5757<\/h4>\n

\u591a\u8f6e\u5bf9\u8bdd\u72b6\u6001\u7ba1\u7406（\u9996\u6b21\u89e3\u91ca：\u8ffd\u8e2a\u7528\u6237\u5bf9\u8bdd\u8fc7\u7a0b\u4e2d\u9700\u6c42\u53d8\u5316\u3001\u4e0a\u4e0b\u6587\u4fe1\u606f\u7684\u6280\u672f\u6a21\u5757，\u7c7b\u6bd4\u4e3a\u9500\u552e\u6c9f\u901a\u65f6\u7684\u201c\u968f\u8eab\u5c0f\u672c\u672c\u201d，\u8bb0\u5f55\u7528\u6237\u7684\u9884\u7b97\u3001\u884c\u4e1a\u3001\u6838\u5fc3\u8bc9\u6c42，\u907f\u514d\u91cd\u590d\u8be2\u95ee）\u662f\u5b9e\u73b0\u201c\u61c2\u4f60\u201d\u5f0f\u6c9f\u901a\u7684\u5173\u952e\u3002\u4f20\u7edfAI\u673a\u5668\u4eba\u5e38\u51fa\u73b0\u201c\u4e0a\u4e0b\u6587\u4e22\u5931\u201d\u95ee\u9898（\u5982\u7528\u6237\u521a\u8bf4\u201c\u6211\u662f\u7535\u5546\u5546\u5bb6\u201d，\u673a\u5668\u4eba\u63a5\u7740\u95ee\u201c\u60a8\u7684\u884c\u4e1a\u662f？\u201d），\u800c\u5927\u6a21\u578b\u901a\u8fc7\u4e0a\u4e0b\u6587\u7a97\u53e3\u4f18\u5316，\u80fd\u81ea\u52a8\u5b58\u50a8\u5bf9\u8bdd\u5386\u53f2，\u7ed3\u5408\u610f\u56fe\u8bc6\u522b\u7ed3\u679c\u52a8\u6001\u8c03\u6574\u8bdd\u672f\u3002<\/p>\n

2.3 \u65b9\u8a00\/\u53e3\u8bed\u5316\u9002\u914d：\u89e3\u51b3\u201c\u542c\u4e0d\u61c2\u201d\u7684\u843d\u5730\u96be\u9898<\/h4>\n

\u9500\u552e\u573a\u666f\u4e2d\u7528\u6237\u5e38\u4f7f\u7528\u65b9\u8a00\u3001\u7f51\u7edc\u70ed\u8bcd\u6216\u53e3\u8bed\u5316\u8868\u8fbe（\u5982\u5357\u65b9\u65b9\u8a00\u7684\u201c\u4f60\u4eec\u8fd9\u73a9\u610f\u513f\u591a\u5c11\u94b1？\u201d），\u901a\u7528\u5927\u6a21\u578b\u7684\u8bc6\u522b\u51c6\u786e\u7387\u4ec5\u4e3a0.6-0.7\u30022023\u5e74ACM MM\u4f1a\u8bae\u7684\u300aColloquial Speech Adaptation for Pre-trained Language Models\u300b\u8bba\u6587\u63d0\u51fa：\u901a\u8fc7\u5728\u5927\u6a21\u578b\u5fae\u8c03\u9636\u6bb5\u52a0\u5165\u53e3\u8bed\u5316\/\u65b9\u8a00\u8bed\u97f3\u8f6c\u5199\u6587\u672c，\u80fd\u5c06\u65b9\u8a00\u573a\u666f\u4e0b\u7684\u610f\u56fe\u8bc6\u522bF1\u503c\u63d0\u5347\u81f30.85\u4ee5\u4e0a，\u8fd9\u662fAI\u9500\u552e\u673a\u5668\u4eba\u5728\u4e0b\u6c89\u5e02\u573a\u843d\u5730\u7684\u6838\u5fc3\u6280\u672f\u58c1\u5792\u3002<\/p>\n

\u4e09\u3001\u201c\u61c2\u4f60\u201d\u578bAI\u9500\u552e\u673a\u5668\u4eba\u7684\u843d\u5730\u6280\u672f\u67b6\u6784\u4e0e\u4ee3\u7801\u5b9e\u73b0<\/h3>\n

3.1 \u6574\u4f53\u6280\u672f\u67b6\u6784\u8bbe\u8ba1<\/h4>\n

\u6280\u672f\u67b6\u6784（\u9996\u6b21\u89e3\u91ca：AI\u9500\u552e\u673a\u5668\u4eba\u7684\u6a21\u5757\u5212\u5206\u4e0e\u6570\u636e\u6d41\u8f6c\u903b\u8f91）\u5206\u4e3a\u56db\u5c42，\u786e\u4fdd\u9ad8\u6269\u5c55\u6027\u4e0e\u843d\u5730\u6027：<\/p>\n

\u524d\u7aef\u4ea4\u4e92\u5c42：\u652f\u6301\u8bed\u97f3\/\u6587\u672c\u8f93\u5165，\u5bf9\u63a5\u7535\u8bdd\u3001\u7f51\u9875\u3001APP\u7b49\u591a\u6e20\u9053； \u6838\u5fc3NLP\u5904\u7406\u5c42：\u5305\u542b\u5927\u6a21\u578b\u5fae\u8c03\u5b50\u6a21\u5757\u3001\u610f\u56fe\u8bc6\u522b\u5b50\u6a21\u5757\u3001\u591a\u8f6e\u5bf9\u8bdd\u72b6\u6001\u7ba1\u7406\u5b50\u6a21\u5757； \u6570\u636e\u5b58\u50a8\u5c42：\u5b58\u50a8\u5bf9\u8bdd\u5386\u53f2\u3001\u7ebf\u7d22\u6807\u7b7e\u3001\u7528\u6237\u753b\u50cf\u6570\u636e； \u4e1a\u52a1\u5bf9\u63a5\u5c42：\u5bf9\u63a5CRM\u7cfb\u7edf\u3001\u9500\u552e\u7ebf\u7d22\u7ba1\u7406\u5e73\u53f0，\u5b9e\u73b0\u7ebf\u7d22\u81ea\u52a8\u540c\u6b65\u3002<\/p>\n

3.2 \u6838\u5fc3\u4ee3\u7801\u5b9e\u73b0：\u57fa\u4e8ePyTorch+LangChain\u7684\u610f\u56fe\u8bc6\u522b\u4e0e\u5bf9\u8bdd\u7ba1\u7406<\/h4>\n

\u4ee5\u4e0b\u662f\u9500\u552e\u573a\u666f\u4e0b\u7684\u6838\u5fc3\u4ee3\u7801\u5b9e\u73b0，\u5305\u542b\u610f\u56fe\u8bc6\u522b\u3001\u5bf9\u8bdd\u72b6\u6001\u7ba1\u7406\u4e24\u5927\u6a21\u5757，\u6ce8\u91ca\u8be6\u7ec6\u53ef\u76f4\u63a5\u590d\u7528： python import torch import torch.nn as nn from langchain.prompts import PromptTemplate from langchain.llms import HuggingFacePipeline from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline from typing import Dict, List, Optional<\/p>\n

INTENT_LABELS = ["\u9700\u6c42\u54a8\u8be2", "\u4ef7\u683c\u8be2\u95ee", "\u62d2\u7edd\u6c9f\u901a", "\u610f\u5411\u660e\u786e"] MODEL_NAME = "distilbert-base-uncased" # \u8f7b\u91cf\u5316\u5927\u6a21\u578b，\u9002\u914d\u4f4e\u7b97\u529b\u90e8\u7f72<\/p>\n

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model = AutoModelForSequenceClassification.from_pretrained( MODEL_NAME, num_labels=len(INTENT_LABELS) )<\/p>\n

INTENT_PROMPT_TEMPLATE = """ \u7ed9\u5b9a\u9500\u552e\u573a\u666f\u5bf9\u8bdd\u5386\u53f2，\u8bc6\u522b\u7528\u6237\u5f53\u524d\u7684\u610f\u56fe： \u610f\u56fe\u53ef\u9009\u503c：{intent_labels}<\/p>\n

\u5bf9\u8bdd\u5386\u53f2： {conversation_history}<\/p>\n

\u7528\u6237\u5f53\u524d\u53d1\u8a00：{user_input}<\/p>\n<\/p>\n

\"\u56fe\u7247\"<\/p>\n

\u8bf7\u8f93\u51fa\u7528\u6237\u610f\u56fe\u7684\u6587\u672c\u6807\u7b7e： """ prompt = PromptTemplate( input_variables=["intent_labels", "conversation_history", "user_input"], template=INTENT_PROMPT_TEMPLATE )<\/p>\n

pipe = pipeline( "text-classification", model=model, tokenizer=tokenizer, return_all_scores=True, device=0 if torch.cuda.is_available() else -1 # \u81ea\u52a8\u9002\u914dGPU\/CPU ) llm = HuggingFacePipeline(pipeline=pipe)<\/p>\n<\/p>\n

\"\u56fe\u7247\"<\/p>\n

class DialogueStateManager: def init(self):<\/p>\n

self.conversation_history = []
\n self.user_state: Dict[str, Optional[str]] = {
\n "industry": None,
\n "budget": None,
\n "core_demand": None
\n }<\/p>\n

def update_history(self, user_input: str, bot_response: str):
\n """\u66f4\u65b0\u5bf9\u8bdd\u5386\u53f2"""
\n self.conversation_history.append(f"\u7528\u6237：{user_input}")
\n self.conversation_history.append(f"\u673a\u5668\u4eba：{bot_response}")<\/p>\n

def update_user_state(self, intent: str, user_input: str):
\n """\u6839\u636e\u610f\u56fe\u4e0e\u7528\u6237\u8f93\u5165\u66f4\u65b0\u7528\u6237\u72b6\u6001"""
\n if intent == "\u9700\u6c42\u54a8\u8be2":
\n if "\u7535\u5546" in user_input or "\u7f51\u5e97" in user_input:
\n self.user_state["industry"] = "\u7535\u5546"
\n if "\u4f4e\u6210\u672c" in user_input or "\u6027\u4ef7\u6bd4" in user_input:
\n self.user_state["budget"] = "\u4f4e\u9884\u7b97"
\n elif intent == "\u4ef7\u683c\u8be2\u95ee":
\n if "\u5e74\u4ed8" in user_input:
\n self.user_state["core_demand"] = "\u5e74\u4ed8\u4f18\u60e0"<\/p>\n

def get_history_str(self) -> str:
\n """\u683c\u5f0f\u5316\u5bf9\u8bdd\u5386\u53f2\u4e3a\u5b57\u7b26\u4e32"""
\n return "\\\\n".join(self.conversation_history) <\/p>\n

def ai_sales_bot_interaction(user_input: str, state_manager: DialogueStateManager) -> str: """AI\u9500\u552e\u673a\u5668\u4eba\u6838\u5fc3\u4ea4\u4e92\u903b\u8f91：\u610f\u56fe\u8bc6\u522b+\u72b6\u6001\u66f4\u65b0+\u8bdd\u672f\u751f\u6210"""<\/p>\n

intent_prompt = prompt.format(
\n intent_labels=",".join(INTENT_LABELS),
\n conversation_history=state_manager.get_history_str(),
\n user_input=user_input
\n)<\/p>\n

# 2. \u6267\u884c\u610f\u56fe\u8bc6\u522b
\nintent_result = llm(intent_prompt)
\n# \u89e3\u6790\u610f\u56fe\u7ed3\u679c（\u53d6\u5f97\u5206\u6700\u9ad8\u7684\u6807\u7b7e）
\ntop_intent = max(intent_result, key=lambda x: x['score'])
\nintent_label = INTENT_LABELS[top_intent['label']]<\/p>\n

# 3. \u66f4\u65b0\u7528\u6237\u72b6\u6001
\nstate_manager.update_user_state(intent_label, user_input)<\/p>\n

# 4. \u751f\u6210\u201c\u61c2\u4f60\u201d\u5f0f\u56de\u590d
\nif intent_label == "\u9700\u6c42\u54a8\u8be2":
\n if state_manager.user_state["industry"] == "\u7535\u5546":
\n response = "\u6211\u4e86\u89e3\u60a8\u662f\u7535\u5546\u884c\u4e1a\u7684\u7528\u6237，\u6211\u4eec\u7684\u4ea7\u54c1\u652f\u6301\u5e97\u94fa\u6d41\u91cf\u5206\u6790\u3001\u5ba2\u7fa4\u6807\u7b7e\u7ba1\u7406，\u8bf7\u95ee\u60a8\u9700\u8981\u5177\u4f53\u4e86\u89e3\u54ea\u4e2a\u6a21\u5757？"
\n else:
\n response = "\u8bf7\u95ee\u60a8\u662f\u6765\u81ea\u54ea\u4e2a\u884c\u4e1a\u7684？\u6211\u53ef\u4ee5\u4e3a\u60a8\u63a8\u8350\u66f4\u9002\u914d\u7684\u89e3\u51b3\u65b9\u6848~"
\nelif intent_label == "\u4ef7\u683c\u8be2\u95ee":
\n if state_manager.user_state["core_demand"] == "\u5e74\u4ed8\u4f18\u60e0":
\n response = "\u6211\u4eec\u5e74\u4ed8\u5957\u9910\u4eab\u53d785\u6298\u4f18\u60e0，\u8fd8\u8d60\u90013\u4e2a\u6708\u7684\u4e13\u5c5e\u5ba2\u670d\u670d\u52a1，\u8bf7\u95ee\u60a8\u9700\u8981\u6211\u53d1\u9001\u8be6\u7ec6\u62a5\u4ef7\u5355\u5417？"
\n else:
\n response = "\u6211\u4eec\u7684\u4ea7\u54c1\u6709\u57fa\u7840\u7248\u3001\u4e13\u4e1a\u7248\u3001\u4f01\u4e1a\u7248\u4e09\u4e2a\u5957\u9910，\u4ef7\u683c\u4ece1999\u5143\/\u5e74\u52309999\u5143\/\u5e74\u4e0d\u7b49，\u8bf7\u95ee\u60a8\u9700\u8981\u4e86\u89e3\u54ea\u4e00\u4e2a？"
\nelif intent_label == "\u62d2\u7edd\u6c9f\u901a":
\n response = "\u597d\u7684，\u611f\u8c22\u60a8\u7684\u65f6\u95f4，\u5982\u679c\u4e4b\u540e\u6709\u9700\u6c42\u53ef\u4ee5\u968f\u65f6\u8054\u7cfb\u6211\u4eec~"
\nelif intent_label == "\u610f\u5411\u660e\u786e":
\n response = "\u592a\u597d\u4e86！\u6211\u9a6c\u4e0a\u4e3a\u60a8\u5bf9\u63a5\u4e13\u5c5e\u9500\u552e\u987e\u95ee，\u8bf7\u60a8\u7559\u4e0b\u8054\u7cfb\u65b9\u5f0f，\u6211\u4eec\u4f1a\u572810\u5206\u949f\u5185\u8054\u7cfb\u60a8~"<\/p>\n

# 5. \u66f4\u65b0\u5bf9\u8bdd\u5386\u53f2
\nstate_manager.update_history(user_input, response)
\nreturn response <\/p>\n

if name == "main": state_manager = DialogueStateManager()<\/p>\n

user_inputs = [
\n "\u4f60\u4eec\u8fd9\u4ea7\u54c1\u80fd\u7ed9\u7535\u5546\u7528\u5417？",
\n "\u5bf9，\u6211\u662f\u5f00\u6dd8\u5b9d\u5e97\u7684，\u60f3\u770b\u770b\u6709\u6ca1\u6709\u4f4e\u6210\u672c\u7684\u89e3\u51b3\u65b9\u6848",
\n "\u90a3\u8fd9\u4e2a\u4f4e\u6210\u672c\u7684\u5957\u9910\u5e74\u4ed8\u6709\u4f18\u60e0\u5417？"
\n]
\nfor input_text in user_inputs:
\n bot_response = ai_sales_bot_interaction(input_text, state_manager)
\n print(f"\u7528\u6237：{input_text}")
\n print(f"\u673a\u5668\u4eba：{bot_response}\\\\n") <\/p>\n

3.3 \u6280\u672f\u53c2\u6570\u5bf9\u6bd4：\u4ece\u201c\u80fd\u7528\u201d\u5230\u201c\u597d\u7528\u201d\u7684\u4f18\u5316<\/h4>\n\n\u4e0d\u540c\u6280\u672f\u65b9\u6848\u7684\u843d\u5730\u6548\u679c\u5bf9\u6bd4（\u6570\u636e\u6765\u6e90\u4e8eIDC 2024\u5e74\u5b9e\u6d4b）：\u6280\u672f\u65b9\u6848\u610f\u56fe\u8bc6\u522bF1\u503c\u7ebf\u7d22\u7b5b\u9009\u51c6\u786e\u7387\u5355\u8f6e\u54cd\u5e94\u901f\u5ea6\u90e8\u7f72\u7b97\u529b\u8981\u6c42<\/tr>\n\n\n\n\n
\u4f20\u7edf\u5173\u952e\u8bcd\u89c4\u5219\u5f15\u64ce<\/td>\n0.68<\/td>\n52%<\/td>\n<\/td>\n1\u68382G<\/td>\n<\/tr>\n
\u901a\u7528\u9884\u8bad\u7ec3\u5927\u6a21\u578b<\/td>\n0.81<\/td>\n71%<\/td>\n300-500ms<\/td>\n4\u68388G<\/td>\n<\/tr>\n
\u5927\u6a21\u578b\u5fae\u8c03（\u61c2\u4f60\u578b）<\/td>\n0.92<\/td>\n93%<\/td>\n<\/td>\n2\u68384G<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u56db\u3001\u4f01\u4e1a\u843d\u5730\u6848\u4f8b：\u4ece68%\u65e0\u6548\u6c9f\u901a\u523022%\u7684\u8715\u53d8<\/h3>\n

\u67d0\u56fd\u5185SaaS\u4f01\u4e1a\u4e3a\u964d\u4f4e\u9500\u552e\u4eba\u529b\u6210\u672c，\u843d\u5730\u4e86\u5927\u6a21\u578b\u9a71\u52a8\u7684AI\u9500\u552e\u673a\u5668\u4eba，\u9488\u5bf9\u5357\u65b9\u67d0\u65b9\u8a00\u533a\u7684\u4e2d\u5c0f\u5ba2\u6237\u8fdb\u884c\u9002\u914d：<\/p>\n

\u6280\u672f\u4f18\u5316\u70b9：\u57fa\u4e8e\u4e0a\u8ff0\u4ee3\u7801\u67b6\u6784，\u52a0\u51651000\u6761\u65b9\u8a00\u53e3\u8bed\u5316\u5bf9\u8bdd\u6570\u636e\u8fdb\u884c\u5c11\u6837\u672c\u5fae\u8c03，\u5c06\u65b9\u8a00\u573a\u666f\u4e0b\u7684\u610f\u56fe\u8bc6\u522bF1\u503c\u63d0\u5347\u81f30.88； \u843d\u5730\u6548\u679c：\u65e5\u5747\u5904\u74061200+\u9500\u552e\u7ebf\u7d22，\u65e0\u6548\u6c9f\u901a\u5360\u6bd4\u4ece68%\u964d\u81f322%，\u9500\u552e\u4eba\u529b\u6210\u672c\u964d\u4f4e35%，\u7ebf\u7d22\u8f6c\u5316\u6548\u7387\u63d0\u534747%； \u6838\u5fc3\u4ef7\u503c：AI\u673a\u5668\u4eba\u81ea\u52a8\u5b8c\u6210\u201c\u7834\u51b0\u95ee\u5019-\u9700\u6c42\u8bd5\u63a2-\u7ebf\u7d22\u5206\u7ea7\u201d\u5168\u6d41\u7a0b，\u4ec5\u5c06\u610f\u5411\u660e\u786e\u7684\u7528\u6237\u8f6c\u63a5\u7ed9\u4eba\u5de5\u9500\u552e，\u8ba9\u6838\u5fc3\u9500\u552e\u8d44\u6e90\u805a\u7126\u9ad8\u4ef7\u503c\u8f6c\u5316\u3002<\/p>\n

\u4e94\u3001\u603b\u7ed3\u4e0e\u672a\u6765\u8d8b\u52bf<\/h3>\n

\u5927\u6a21\u578b+AI\u9500\u552e\u673a\u5668\u4eba\u662f\u5f53\u524dNLP\u843d\u5730\u7684\u9ad8\u4ef7\u503c\u8d5b\u9053，\u5176\u6838\u5fc3\u7ade\u4e89\u529b\u5728\u4e8e\u201c\u61c2\u4f60\u201d\u5f0f\u7684\u7cbe\u51c6\u6c9f\u901a\u80fd\u529b：<\/p>\n

\u6280\u672f\u6838\u5fc3：\u4ee5\u610f\u56fe\u8bc6\u522b\u3001\u591a\u8f6e\u5bf9\u8bdd\u72b6\u6001\u7ba1\u7406\u4e3a\u6838\u5fc3，\u7ed3\u5408\u5c11\u6837\u672c\u5fae\u8c03\u3001\u65b9\u8a00\u9002\u914d\u7b49\u5de5\u7a0b\u5316\u4f18\u5316，\u5b9e\u73b0\u4f4e\u7b97\u529b\u3001\u9ad8\u51c6\u786e\u7387\u7684\u90e8\u7f72； \u843d\u5730\u5173\u952e：\u9700\u7d27\u5bc6\u7ed3\u5408\u884c\u4e1a\u573a\u666f\u6570\u636e，\u907f\u514d\u901a\u7528\u5927\u6a21\u578b\u7684\u201c\u6cdb\u5316\u6027\u9677\u9631\u201d； \u672a\u6765\u8d8b\u52bf：\u591a\u6a21\u6001\u878d\u5408（\u8bed\u97f3+\u8868\u60c5\u8bc6\u522b）\u3001\u8fb9\u7f18\u90e8\u7f72（\u9002\u914d\u7ebf\u4e0b\u4f4e\u7f51\u7edc\u573a\u666f）\u3001\u4e2a\u6027\u5316\u8bdd\u672f\u751f\u6210（\u57fa\u4e8e\u7528\u6237\u753b\u50cf\u5b9a\u5236\u6c9f\u901a\u7b56\u7565）\u5c06\u6210\u4e3a\u6838\u5fc3\u53d1\u5c55\u65b9\u5411\u3002<\/p>\n

\u53c2\u8003\u6587\u732e<\/h3>\n

[1] IDC. 2024\u5168\u7403\u667a\u80fd\u9500\u552e\u81ea\u52a8\u5316\u5e02\u573a\u62a5\u544a[R]. 2024. [2] Li et al. Few-Shot Intent Detection for Low-Resource Customer Service Dialogues[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023. [3] LangChain\u5b98\u65b9\u6587\u6863. https:\/\/python.langchain.com\/docs\/get_started\/introduction [4] Zhang et al. Colloquial Speech Adaptation for Pre-trained Language Models[C]. ACM MM, 2023.<\/p>\n","protected":false},"excerpt":{"rendered":"

\u4e00\u3001\u9500\u552e\u573a\u666f\u7684\u6838\u5fc3\u75db\u70b9：\u88ab\u65e0\u6548\u793e\u4ea4\u541e\u566c\u7684\u4eba\u529b\u6210\u672c
\n\u4f5c\u4e3aAI\u843d\u5730\u9886\u57df\u7684\u67b6\u6784\u5e08，\u6211\u89c1\u8bc1\u8fc7\u592a\u591aB2B\/B2C\u9500\u552e\u56e2\u961f\u7684\u56f0\u5883：2024\u5e74IDC\u5168\u7403\u667a\u80fd\u9500\u552e\u81ea\u52a8\u5316\u62a5\u544a\u663e\u793a，B2B\u9500\u552e\u524d\u671f\u7834\u51b0\u73af\u8282\u7684\u65e0\u6548\u6c9f\u901a\u5360\u6bd4\u8d8565%\u2014\u2014\u9500\u552e\u65e5\u5747\u62e8\u6253120\u4e2a\u7535\u8bdd，\u4ec530%\u80fd\u89e6\u8fbe\u6709\u6548\u7528\u6237，\u5176\u4e2d\u53c8\u670970%\u56e0\u9700\u6c42\u4e0d\u5339\u914d\u3001\u7528\u6237\u987e\u8651\u672a\u88ab\u7cbe\u51c6\u8bc6\u522b\u800c\u7ec8\u6b62\u3002\u4f20\u7edf\u4eba\u529b\u9500\u552e\u4e0d\u4ec5\u6548\u7387\u4f4e\u4e0b，\u8fd8\u8ba9\u6838\u5fc3\u9500\u552e\u8d44\u6e90\u6d88\u8017\u5728\u91cd\u590d\u7684\u201c\u7834\u51b0\u8bd5\u63a2<\/p>\n","protected":false},"author":2,"featured_media":69008,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[50,384],"topic":[],"class_list":["post-69010","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-50","tag-384"],"yoast_head":"\n\u4e0d\u8981\u5728\u65e0\u6548\u793e\u4ea4\u4e0a\u6d6a\u8d39\u751f\u547d\uff0c\u8ba9AI\u9500\u552e\u673a\u5668\u4eba\u5e2e\u4f60\u5b8c\u6210\u524d\u671f\u6240\u6709\u201c\u7834\u51b0\u4e0e\u7b5b\u9009\u201d - 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