introduce
Welcome to the dynamic world of generative AI and language models (LLMs)! In this comprehensive exploration, we'll delve into the intricacies of generative AI, foundational models (FM), prompt engineering, LangChain, vector databases, illusions, reasoning, and responsible AI. The landscape is rapidly evolving, and understanding these concepts is critical to navigating the cutting-edge developments of AI and ML.
Chapter 1: Clarifying Generative AI
Beyond the evolution of discriminative AI
Historically dominated by discriminative AI, where models classify or make predictions based on training data, the emergence of generative AI marks a paradigm shift. Now, models can create new content and make great strides in various industries such as fashion, automotive, finance, healthcare, and more. Deep learning and NLP have evolved from traditional models to complex models such as GANs (Generative Adversarial Networks) that contribute to artificial general intelligence (AGI).
Generative AI is a transformative field that has gained tremendous momentum by creating content using large amounts of pre-existing data. Outside the realm of tech enthusiasts, it democratizes the implementation of artificial intelligence, eliminating the need for non-technical people to learn programming. Coders and programmers benefit not only from code generation models, but also from translation across programming languages. However, researchers face challenges in keeping up with the pace of progress. Automation is reshaping every field of work, from call centers to content creation, with AI-generated voice assistants, 3D imaging, AR/VR, the metaverse, robotics, and self-driving cars at the forefront.
Synthetic data creation for model training is another breakthrough that enhances privacy by generating real-world-like data. With the advent of generative models, film and visual editing have undergone a fascinating shift, bringing with them the possibility of entertainment for historical scenes, aging characters in pictures, and changing backgrounds and costumes. The promise of AI has advanced to the point where models can summarize or generate questions from large amounts of text, reducing the need for manual reading.
The democratization of artificial intelligence
Generative AI democratizes AI enforcement and makes it accessible to non-technical people. Coders and non-coders alike can benefit from generating code, translating programming languages, and models that help with content creation. While it simplifies the task for some, researchers face challenges in keeping up with the pace of progress.
Chapter 2: Demystifying the Base Model
Learn about the underlying model
The base model (FM) represents a shift from traditional machine learning models. Trained on a large number of unlabeled data using a self-supervised approach, FM exhibits emergent and homogenized characteristics. OpenAI's GPT-4, DALL-E2, and StabilityAI's Stable Diffusion are well-known FMs that have revolutionized tasks like text generation, translation, and code completion.
Build a base model
To build an FM like ChatGPT, a multi-step process involves pre-training a foundational language model on massive datasets from the internet. Training on distributed GPUs typically lasts several months, laying the groundwork for training. Techniques such as prompt engineering, supervised fine-tuning, reward modeling, and reinforcement learning can augment and fine-tune the underlying model.
Traditional machine learning models operate on the confines of supervised learning, trained on labeled data, and used for specific tasks such as image recognition or sentiment analysis. The basic model developed in recent years is trained on large amounts of unlabeled data by self-supervised or semi-supervised, allowing it to adapt to different tasks, including discriminative and generative tasks. Created by the Center for Basic Model Research (CRFM), these models exhibit emergences and homogeneity, demonstrate unexpected characteristics, and apply the same approach in various fields.
The much-hyped base models include OpenAI's GPT-4, Dall-E2, and StabilityAI Stable Diffusion. Building such models involves pre-training on large-scale data, primarily from cloud service providers, and subsequent steps such as prompt engineering, supervised fine-tuning, reward modeling, and reinforcement learning based on human feedback. These models demonstrate that the duration of training, rather than the number of parameters, can significantly affect performance.
Chapter 3: Focus on large language models
The power of large language models
Large language models (LLMs) are a subset of FM that are trained on a large corpus of text and are capable of human-like conversations. The Transformer model introduced by Google in "Attention is All You Need" has paved the way for LLMs such as OpenAI's GPT family, HuggingFace's BLOOM, Google's PALM2, and Meta AI's LLAMA.
Large language models (LLMs) are part of the base model and are trained on massive text corpora, often with billions of parameters. Derived from Google's "Attention is All You Need!" and Transformer models, LLMs excel at understanding natural language, word meaning, and relevance. Notable models include OpenAI's GPT series, HuggingFace BLOOM, Google's PALM2, Meta AI's LLAMA, and other models from Cohere and AI24Labs. LLMs can be fine-tuned on small supervised datasets using Parameter Efficient Fine-Tuning (PEFT) to minimize compute and storage costs.
Fine-tuning and efficient parameter usage
Fine-tuning LLMs involves using domain-specific data to adapt them to specific tasks. Efficient Parameter Tuning (PEFT) solves the challenges in fine-tuning and reduces compute and storage costs. Reward modeling based on human feedback and reinforcement learning further optimize LLMs for complex tasks.
Chapter 4: The Art of Prompt Engineering
Unleash the power of cues
Prompt-based ML facilitates interaction with LLMs by describing tasks through prompts. Techniques such as zero-shot prompts, few-shot prompts, chain-of-thought (CoT) prompts, and self-consistency improve accuracy. Prompts are an important tool to guide LLMs in a variety of applications, from code generation to text summarization.
Prompt-based machine learning facilitates interaction with LLMs by sending requests describing the expected task. Precise prompts produce better results, and various techniques have emerged, such as zero-shot prompts, few-shot prompts, chain of thought (CoT), self-consistency, and thought trees. Fine-tuning LLMs with domain-specific data using the prompt and completion format still works, allowing for specific tasks.
LangChain:交互框架
LangChain is a powerful LLM framework that powers models from various labs. With modules such as model hubs, data connections, chains, proxies, memory, and callbacks, LangChain simplifies LLM application development in Javascript and Python environments.
LangChain is a framework that supports LLM interactions, with modules such as model hub, data connection, chain, proxy, memory, and callback. With the support of AI24Labs, Anthropic, Cohere, HuggingFace, OpenAI, and others, LangChain simplifies the development of LLM applications. It is capable of processing and organizing information in documents, complemented by vector databases and embeddings that enhance contextual preservation.
Chapter 5: Leveraging Vector Databases and Embedding
Efficient data storage and retrieval
Vector databases optimized for high-dimensional vector storage play a crucial role in LLM applications. Platforms such as Milvus, Pinecone, and FAISS enable efficient storage and retrieval, supporting tasks such as vector distance-based image and document searches.
Embedding: Bridging complexity and understanding
Embedding converts complex data types into numerical representations that help deep neural networks understand and process data. Benefits include dimensionality reduction, semantic relation capture, and support for tasks such as recommender systems and text classification.
A vector database optimized for storing and retrieving high-dimensional vectors is used to efficiently store LLM-generated vectors. Implementations such as Milvus, Pinecone, Weaviate, Annoy, and FAISS can quickly retrieve domain-specific data. The structured representation of data in mathematical vectors allows for efficient similarity search and processing of various data types, which is helpful for tasks such as recommender systems, text classification, information retrieval, and clustering.
Chapter 6: Hallucinations and Grounding
Solving the LLM Illusion
LLMs often produce hallucinatory outputs due to factors such as overgeneralization, lack of contextual understanding, biased training data, and encountering rare inputs. Techniques such as fine-tuning, adversarial testing, and transparent model interpretation are employed to reduce the risk of hallucinations.
Grounding LLMs to ensure reliability
LLM grounding is essential for reliable output. Reinforcement learning through human feedback, external knowledge integration, and fact-checking mechanisms can help develop grounding strategies. Ongoing research explores ways to enhance contextual comprehension and reasoning.
LLMs are prone to hallucinations, resulting in answers that seem plausible but incorrect. Reasons include overgeneralization, lack of contextual understanding, biased training data, and encountering rare or out-of-distribution inputs. Grounding LLMs remains a challenge, while techniques like reinforcement learning for human feedback and fact-checking for other LLMs aim to mitigate hallucinations. Ongoing research highlights the need for diverse and representative training data, adversarial testing, and transparent model interpretation.
Chapter 7: Introducing SocraticAI for Inference
Multi-agent collaborative problem solving
SocraticAI introduces multi-agent collaboration, involving analysts like Socrates and Theaetetus, as well as proofreader Plato. This collaborative role-playing framework built on ChatGPT 3.5 combines WolframAlpha and a Python code interpreter to solve problems. SocraticAI aims to bring inference to LLMs in mathematical and logical tasks.
SocraticAI brings inference to LLMs through multi-agent collaborative problem solving. It utilizes three proxies in the form of a role-play—Socrates, Theetus, and Plato—that combines WolframAlpha and a Python code interpreter. SocraticAI aims to enhance reasoning in logic-based tasks and provide a structured approach to problem-solving. Key features include question generation, conversation management, domain knowledge integration, adaptive learning, and feedback/evaluation mechanisms.
Chapter 8: Pioneering Responsible Generative AI
Reduce risk and ensure ethical AI
Generative AI comes with risks such as biased output, data privacy issues, and misinformation spread. Responsible AI practices include understanding potential risks, establishing ethical guidelines, transparent model interpretation, bias detection, user control, and continuous monitoring. Collaboration and adherence to ethical standards within the AI community are essential for responsible AI development.
conclusion
As we navigate the vast field of generative AI and LLMs, we're witnessing the transformative power of technology. From the evolution of discriminative AI to the rise of foundational models, the art of rapid engineering, and the challenge of illusion, responsible AI practices guide us towards ethical and sustainable development. Through continuous advancement, collaborative efforts, and a commitment to responsible AI, we shape a future where generative models make a positive contribution to society.