Google Engineers On Learnings From Building Gemini
TLDRIn this fireside chat, Google engineers James Rubin, Peter Gabi, and Peter Danenberg discuss the challenges and solutions in productionizing enterprise-ready large language models (LLMs). They emphasize the practical applications of LLMs, which are essentially advanced autocomplete systems capable of next word prediction and can be adapted for various machine learning tasks. The conversation highlights the importance of customizability, the reduction in time and resources needed to train models, and the potential of few-shot learning. The engineers also touch on the complexities of domain adaptation, supervised fine-tuning, and the trade-offs between factuality and creativity in AI applications. Data privacy is a key concern, with recommendations to use retrieval-augmented generation and guardrails to protect sensitive information. The discussion provides insights into the practical use of LLMs in startups and the balance between safety features and utility.
Takeaways
- 🌟 **Enterprise AI Adoption**: Most enterprises prefer to build AI apps in-house on top of foundational models rather than using off-the-shelf B2B AI software.
- 🚀 **Gemini's Role**: Google's Gemini is focused on productionizing enterprise-ready large language models (LLMs) and addressing the challenges that come with it.
- 🤖 **LLM Definition**: LLMs are akin to advanced autocomplete systems that can predict the next word in a sequence, scaling up to understand longer contexts with more parameters.
- 📚 **Zero-Shot Learning**: LLMs can perform zero-shot or few-shot learning, requiring fewer examples to understand new concepts compared to traditional machine learning methods.
- ✅ **Task Reframing**: Traditional machine learning problems can be reframed as word prediction problems, allowing LLMs to tackle a wider range of applications.
- 🚦 **Customizability**: Businesses value the ability to customize LLMs for specific tasks or domains, which is crucial for addressing vertical use cases and customer problems.
- 🔍 **Metrics and Evaluation**: Establishing clear metrics is essential for evaluating the performance of LLMs and guiding their improvement over time.
- 🎓 **Educational Impact**: Peter G. Bowski created a foundational LLM boot camp during the AI boom, which has been taken by tens of thousands, reflecting the importance of continuous learning.
- 📈 **Startup Acceleration**: Startups are leveraging LLMs to reduce time to market for new AI applications, training models with fewer examples and achieving results faster.
- 🛡️ **Data Privacy Concerns**: Enterprises are wary of training models on sensitive data, which can be mitigated by using techniques like retrieval-augmented generation to protect privacy.
- ⚖️ **Factual vs. Creative**: There's a balance to be struck between the factuality and creativity of LLMs, especially in applications like personal companions where the quality is more ambiguous.
Q & A
What is the main focus of the discussion in the transcript?
-The main focus of the discussion is on the challenges and solutions related to productionizing enterprise-ready large language models (LLMs), with an emphasis on practical applications and the disconnect between the enthusiasm for building AI apps in-house and the hesitance to deploy LLMs externally in production.
What does Peter G. describe large language models (LLMs) as?
-Peter G. describes large language models (LLMs) as 'fancy autocomplete', highlighting their ability to predict the next word in a sequence, which can be applied to a wide range of traditional machine learning tasks.
How do LLMs demonstrate zero-shot or few-shot learning?
-LLMs demonstrate zero-shot or few-shot learning by being able to understand and perform tasks after being shown very few examples, similar to how a young child can learn to identify objects after being shown only a couple of instances.
What is the significance of the shift in LLMs making them attractive to businesses?
-The shift in LLMs that makes them attractive to businesses is the dramatic reduction in the time, data, expertise, and compute resources required to train a model to a fixed quality. This allows for quicker time to market and more efficient use of resources.
Why is customizability important for businesses using LLMs?
-Customizability is important for businesses as it allows them to tune and align models to specific tasks or domains, addressing their unique use cases and customer problems more effectively.
What are some advanced approaches businesses can take to specialize LLMs in their domain?
-Advanced approaches include domain adaptation techniques such as continued pre-training on a corpus of data relevant to the business domain, and supervised fine-tuning (SFT) for improving performance on very specific tasks.
How does Peter G. describe the process of training an LLM for a specific task?
-Peter G. describes the process as starting with identifying the problem the business wants to solve using a large model, then beginning by asking the model questions or setting up a sandbox environment. Businesses should run internal pilots, set metrics, gather data, and iteratively improve the model's performance on domain-specific tasks.
What is the phenomenon of 'hallucination' in chatbots, and how can it be addressed?
-Hallucination in chatbots refers to the model generating false or incorrect information when it is unsure of the correct response. This can be addressed by using techniques like retrieval-augmented generation, which combines the strengths of language models and databases to provide accurate and factual responses.
Why is data privacy a significant concern for enterprises when using LLMs?
-Data privacy is a significant concern due to the potential for sensitive or proprietary information to be inadvertently revealed during the training or prompting of LLMs. Enterprises are concerned about maintaining the confidentiality of their data and complying with data protection regulations.
How can enterprises ensure data privacy while still benefiting from LLMs?
-Enterprises can ensure data privacy by using a retrieval-augmented generation framework, which allows sensitive data to be stored securely in a database and only injected into the model's response when needed. This approach helps maintain data privacy while still leveraging the capabilities of LLMs.
What is the trade-off between factuality and creativity in LLMs, and how can it be balanced?
-The trade-off between factuality and creativity in LLMs involves ensuring that the model provides accurate and helpful responses without compromising on safety or utility. Balancing this involves setting appropriate guardrails or policy layers on top of the model's outputs and possibly involving human oversight to evaluate the model's responses, especially in creative or ambiguous use cases.
What are guardrails in the context of LLMs, and why are they important?
-Guardrails in the context of LLMs are policies or constraints applied to the model's outputs to control its behavior and ensure safety. They are important to prevent the model from generating harmful, inaccurate, or inappropriate content, and to align the model's responses with the business's goals and values.
Outlines
😀 Introduction to the Panel and Discussion Focus
James Rubin introduces himself as the moderator for the fireside chat and is joined by esteemed colleagues Peter Gabi and Peter Danenberg. They aim to discuss the challenges and solutions in productionizing enterprise-ready large language models (LLMs) and state-of-the-art approaches to overcome these challenges. The conversation is intended to be practical and serve as a starting point for further discussions. Rubin shares his background, and both Peters introduce their roles and experiences at Google, including Peter G. Bowski's teaching role at UC Berkeley. They highlight a recent survey indicating a trend of enterprises building AI apps in-house and the lag in deploying LLMs for external apps, which is the focus of their discussion. Peter G. explains LLMs as advanced autocomplete systems capable of next word prediction and discusses their potential for various machine learning tasks.
🚀 Accelerating Time to Market with LLMs
The panelists discuss how startups are leveraging LLMs to accelerate the time to market for their AI applications. Peter G. shares insights on how startups are training smaller models, or 'baby models,' for classification tasks, reducing the time from months to weeks. They also touch upon the importance of customizability in LLMs, allowing businesses to align models with specific tasks or domains. Peter D. emphasizes the complexity of customization and the need for businesses to navigate this with the right techniques and understanding of quality and cost trade-offs. Peter G. suggests starting with a clear problem statement and using few-shot prompting techniques as a starting point for businesses looking to specialize their LLMs.
🤖 Advanced Techniques for Domain Specificity and Task Framing
The conversation delves into advanced techniques for making LLMs more domain-specific and task-focused. Peter G. discusses domain adaptation through continued pre-training on a corpus of data relevant to the model's intended use, comparing it to a law student reading legal textbooks. He also mentions supervised fine-tuning (SFT) for task-specific improvements. Peter D. raises the issue of ambiguous quality definitions in some use cases, sharing an anecdote about fine-tuning a model to emulate Sherlock Holmes. They discuss the importance of human evaluation in the loop for such cases and the concept of 'method acting' for LLMs, where fine-tuning is akin to actors immersing themselves in their roles.
📉 Experiments with LLMs in Complex Workflows and Factual Accuracy
The panelists share their experiences with using LLMs in complex and creative workflows. Peter G. talks about an experiment where an LLM was trained as an asynchronous day trading bot, with mixed results. They discuss the importance of factuality in enterprise applications, citing an example of an airline chatbot that provided incorrect information, leading to a lawsuit. Peter G. explains the concept of 'hallucination' in chatbots and the role of instruction tuning in making models more helpful, which can inadvertently lead to the generation of false information. They propose retrieval-augmented generation and guardrails as solutions to mitigate these issues.
🛡️ Data Privacy Concerns and Solutions
The discussion addresses the critical issue of data privacy in the context of LLMs. Peter G. advises against training models on sensitive data and suggests using a retrieval-augmented generation framework to handle sensitive information securely. He also mentions the benefits of this approach in complying with data privacy regulations like GDPR and HIPAA. Peter D. highlights the mistrust some businesses have towards closed-source model providers, fearing their sensitive data could be used for training purposes. The panelists emphasize the importance of data discipline and the role of open-source models in maintaining control over data privacy.
Mindmap
Keywords
💡Gemini
💡Large Language Models (LLMs)
💡Productization
💡Zero-shot/Few-shot Learning
💡Customizability
💡Data Privacy
💡Retrieval-Augmented Generation (RAG)
💡Guardrails
💡Factualness
💡Enterprise AI Adoption
💡Domain Adaptation
Highlights
Google engineers discuss the challenges and solutions in productionizing enterprise-ready large language models (LLMs).
LLMs are essentially advanced autocomplete systems capable of next word prediction with increasing context windows.
LLMs can be applied to a wide range of traditional machine learning approaches and tasks.
Zero-shot or few-shot learning allows LLMs to make predictions with significantly less data than traditional methods.
Startups are using 'baby models' or smaller LLMs for rapid deployment in classification tasks.
Customizability is a top selection criterion for enterprises, allowing models to be tuned for specific tasks or domains.
Peter G. Bowski highlights the shift towards faster training times, less data requirements, and reduced expertise needed for LLMs.
Domain adaptation and continued pre-training are methods for improving domain specificity in LLMs.
Supervised fine-tuning (SFT) is recommended for improving specific task performance in LLMs.
The importance of having metrics in place to measure improvements when customizing and deploying LLMs.
LLMs can be extended for complex workflows, operating asynchronously, synchronously, or even autonomously.
An example of an LLM being trained as an asynchronous day trading bot with function calling.
Factuality is crucial for enterprises, and LLMs can hallucinate or make up information when uncertain.
Retrieval-augmented generation is a technique to combine LLMs with databases for factual responses.
Guardrails are essential for controlling the behavior of stochastic machine learning models like LLMs.
Data privacy is a primary concern for enterprises, with customizability and security being top selection criteria for model providers.
Recommendations for handling sensitive data include using retrieval-augmented generation frameworks and ensuring data privacy regulations compliance.
Mistrust of businesses using closed-source model providers due to concerns over data privacy and potential misuse of logs.
The balance between factuality and creativity in LLMs, especially in startups focusing on AI personas.
The conversation emphasizes the complexity of deploying LLMs in production environments, touching on practical applications and theoretical insights.