LLM vs traditional NLP/ML/SDE

Traditional software/ML/NLP are precise, rule-driven, and task-specific.
LLMs / AIGC are general-purpose, context-aware, and capable of intelligent generation and interaction.
Adopting LLMs and AIGC helps enterprises boost productivity, automate processes, enhance creativity, and improve user experiences.
Recommended entry points:
- Customer service bots
- Internal knowledge assistants
- Marketing content generation
- AI pair programming tools
- AI-powered analytics interfaces

LLM / AIGC vs. Traditional Software, ML, and NLP

Category	Examples	Core Characteristics	Data/Training Dependency	Capability Scope
Traditional Software	ERP, CRM, Web Apps	Rule-based, deterministic logic	No training needed, logic is explicitly coded	Limited to predefined tasks (e.g., reporting, workflows)
Traditional ML	Linear regression, decision trees, SVM	Feature-driven, narrow generalization	Requires manual feature engineering and labeled data	Task-specific optimization (e.g., demand forecasting)
Traditional NLP	TF-IDF, LSTM, CRF	Task-specific, modular approach	Needs labeled corpora for each task	Poor generalization, brittle across tasks
LLMs (Large Language Models)	GPT, Claude, Gemini	Pretraining + fine-tuning, self-supervised	Trained on large-scale unlabeled text	Strong generalization, few/zero-shot across tasks
AIGC (AI-Generated Content)	Text, image, video, code generation	Generative, often multimodal	Depends on massive, diverse datasets	Capable of creating novel content beyond automation

Task	Data Type	Representative Tech	Sample Task
Structured	Table, Database	Traditional ML、SQL	Customer Ratiing Prediction; Price Prediction
Unstructured	Text, Image, Voice	NLP、LLM、CV	Intelligent Answer, Content Generation, Image Recognition

类型	常见任务	指标	特点
NLU	分类/问答等	Accuracy, F1, EM, Span-F1	语义理解能力评估，评估系统对文本语义的理解能力，常用于分类、实体识别、问答等任务。
文本生成任务	摘要/翻译/写作	BLEU、ROUGE、METEOR、BERTScore	测文本生成与参考文本的相似度，e.g. 计算生成文本与一个或多个参考文本之间的 n-gram 精确匹配率。
模型本身评估	语言建模	Perplexity	越低越好，表示模型越“不困惑”