Thank you so much! Stanford CS25: V3 I Beyond LLMs: Agents, Emergent Abilities, Intermediate-Guided Reasoning, BabyLM I. Introduction to Agents and Language Models 00:05 🤖 The lecture focuses on key topics in Transformers and Large Language Models (LLMs), including emergent abilities, intermediate guided reasoning, and the concept of "baby LM." A. Emergence of Abilities in Large Language Models (LLMs) 1. Emergent Abilities of Large Language Models 2. Key Findings: a) Abilities present in larger models but not smaller ones b) No direct prediction from extrapolating smaller model performance c) Performance jumps at certain training flops threshold 00:33 🌟 Emergent abilities in LLMs are identified as skills that become apparent in large models but not in smaller ones, and are not predictable by extrapolating from smaller models' performance. B. Examples of Emergent Abilities 1. Modularity arithmetic 2. Unscrambling words 3. Question-answering tasks 01:44 🔍 A significant performance leap in LLMs is observed at around 10 to the 22 or 23 training flops, showcasing a non-linear improvement in capabilities. C. Challenges and Risks of Emerging Abilities 1. Unpredictability and extrapolation limitations 2. Evaluation metrics may not fully explain emergence 3. Potential for bias, toxicity, and memorization of training data 4. Societal and ethical considerations 5. Shifts in NLP community towards general-purpose models 03:21 🔬 To foster emergent abilities in LLMs, research is focusing on new architectures, high-quality data, improved training methods, and understanding of F-shot prompting abilities. 04:01 ⚠ Societal risks associated with larger LLMs include bias, toxicity, and truthfulness issues, necessitating careful and responsible advancements in the field. 04:57 🌍 The rise of general-purpose models like GPT-3.5 and GPT-4 has shifted the NLP community's focus from task-specific to versatile, multi-application models. 06:07 📉 A potential point of diminishing returns in model scaling is anticipated, with increased emphasis on data quality and innovative training techniques. 07:45 🧠 Intermediate guided reasoning, a broader term for Chain of Thought reasoning, is discussed for enhancing LLM performance on complex tasks. II. Intermediate Guided Reasoning (IGR) A. Inspiration: Chain-of-Thought (CoT) Reasoning 1. Improves LLM performance on complex reasoning tasks 2. Simulates human thought process by decomposing problems B. Advantages of IGR 1. Provides interpretable window into model behavior 2. Exploits deeper model knowledge beyond simple prompting C. Challenges of IGR for Smaller Models 1. Fundamentally limited capabilities 2. Failure at relatively easier tasks 3. Logical loopholes and infinite loops D. Potential Extensions and Generalizations of IGR 1. Tree of Thought (ToT) for multiple reasoning paths 2. Socratic questioning for divide-and-conquer algorithms 3. Program-based reasoning with code generation 15:22 🚼 The "baby LM" challenge aims to train smaller LLMs with limited data, similar to the linguistic exposure of a child, to improve efficiency and accessibility in research. III. Baby Language Model (BabyLM) Challenge A. Motivation for BabyLM 1. Diminishing returns and challenges of scaling up models 2. Limited accessibility and high costs of large models 3. Goal: Train smaller models on same linguistic data as a child B. BabyLM Training Data 1. Developmentally inspired pre-training dataset 2. Less than 100 million words, mostly transcribed speech 3. Mixed-domain to reflect child's language exposure 19:29 🤖 AI agents represent a shift from traditional LLMs, focusing on more dynamic interactions, personalization, and practical applications in various domains. IV. Agents for Natural Language Processing A. Thesis: Humans will communicate with AI using natural language 1. Current interaction methods (e.g., websites, UIs) are inefficient 2. AI agents can simplify interactions and act as digital extensions 27:44 🧑💼 Personalization and user-agent alignment are crucial in developing AI agents, ensuring they cater to individual preferences and requirements. B. Why Build Agents? 1. Chaining, recursion, and multiple calls to LLM are often needed 2. Agents can solve these challenges and act as a computing chip C. Agent Architecture 1. Key components: memory, tools, planning layer, and interface 2. Example: Browser agent for autonomously booking a flight D. Levels of Autonomy for Agents 1. Five levels defined, similar to autonomous driving classification 2. Current state of software agents is around Level 2 or 3 3. Transition towards Level 4 and 5 systems expected in the future 31:14 🖥 Computer interactions with AI agents can be achieved through APIs or direct control, each with its advantages and challenges in implementation. V. Computer Interactions with Agents A. Two Approaches for Agent-Computer Interaction 1. API-based: Using existing APIs and tools programmatically a) Advantages: Easy to learn, safe, and controllable b) Disadvantages: Limited flexibility and potential for errors 2. Direct Interaction: Using keyboard and mouse control a) Advantages: More performant and flexible b) Disadvantages: More prone to errors and safety concerns B. Universal API for Agent Invocation 1. Exploration of a single API to invoke any agent 2. Eliminates the need for individual APIs for each task VI. Memory and Personalization for Agents A. Memory for Agents 1. Analogy to disk storage in a computer 2. Challenges: Long-term storage, temporal coherence, and structured data B. Personalization and User-Agent Alignment 1. Importance of understanding and aligning with user preferences 2. Explicit and implicit learning methods for personalization 3. Challenges: Data collection, feedback mechanisms, and flat application 42:11 🗣 Multi-agent communication and coordination are essential for complex task execution, requiring robust protocols and hierarchical management. VII. Multi-Agent Systems A. Advantages of Multi-Agent Systems 1. Parallelization and task specialization 2. Improved efficiency and scalability B. Challenges in Multi-Agent Communication 1. Communication protocols for effective information exchange 2. Hierarchies and task coordination 3. Robustness against miscommunication and errors 50:22 📈 Future directions in AI agent development include addressing reliability, error correction, security, and user permissions to ensure safe and effective deployment. VIII. Future Directions and Key Issues A. Key Issues with Building Autonomous Agents 1. Reliability and error reduction 2. Looping problems and divergence from tasks 3. Testing, benchmarking, and deployment challenges 4. Observability and kill switch mechanisms for safety B. Computer Abstraction of Agents 1. LM Operating System concept 2. Neural Computer architecture with chat interface, task engine, and routing agent C. Importance of Error Correction and Security 1. Inherent error correction mechanisms in agent frameworks 2. User permissions and sandboxing for security 3. Safe deployment in risky settings
Thank you so much!
Stanford CS25: V3 I Beyond LLMs: Agents, Emergent Abilities, Intermediate-Guided Reasoning, BabyLM
I. Introduction to Agents and Language Models
00:05 🤖 The lecture focuses on key topics in Transformers and Large Language Models (LLMs), including emergent abilities, intermediate guided reasoning, and the concept of "baby LM."
A. Emergence of Abilities in Large Language Models (LLMs)
1. Emergent Abilities of Large Language Models
2. Key Findings:
a) Abilities present in larger models but not smaller ones
b) No direct prediction from extrapolating smaller model performance
c) Performance jumps at certain training flops threshold
00:33 🌟 Emergent abilities in LLMs are identified as skills that become apparent in large models but not in smaller ones, and are not predictable by extrapolating from smaller models' performance.
B. Examples of Emergent Abilities
1. Modularity arithmetic
2. Unscrambling words
3. Question-answering tasks
01:44 🔍 A significant performance leap in LLMs is observed at around 10 to the 22 or 23 training flops, showcasing a non-linear improvement in capabilities.
C. Challenges and Risks of Emerging Abilities
1. Unpredictability and extrapolation limitations
2. Evaluation metrics may not fully explain emergence
3. Potential for bias, toxicity, and memorization of training data
4. Societal and ethical considerations
5. Shifts in NLP community towards general-purpose models
03:21 🔬 To foster emergent abilities in LLMs, research is focusing on new architectures, high-quality data, improved training methods, and understanding of F-shot prompting abilities.
04:01 ⚠ Societal risks associated with larger LLMs include bias, toxicity, and truthfulness issues, necessitating careful and responsible advancements in the field.
04:57 🌍 The rise of general-purpose models like GPT-3.5 and GPT-4 has shifted the NLP community's focus from task-specific to versatile, multi-application models.
06:07 📉 A potential point of diminishing returns in model scaling is anticipated, with increased emphasis on data quality and innovative training techniques.
07:45 🧠 Intermediate guided reasoning, a broader term for Chain of Thought reasoning, is discussed for enhancing LLM performance on complex tasks.
II. Intermediate Guided Reasoning (IGR)
A. Inspiration: Chain-of-Thought (CoT) Reasoning
1. Improves LLM performance on complex reasoning tasks
2. Simulates human thought process by decomposing problems
B. Advantages of IGR
1. Provides interpretable window into model behavior
2. Exploits deeper model knowledge beyond simple prompting
C. Challenges of IGR for Smaller Models
1. Fundamentally limited capabilities
2. Failure at relatively easier tasks
3. Logical loopholes and infinite loops
D. Potential Extensions and Generalizations of IGR
1. Tree of Thought (ToT) for multiple reasoning paths
2. Socratic questioning for divide-and-conquer algorithms
3. Program-based reasoning with code generation
15:22 🚼 The "baby LM" challenge aims to train smaller LLMs with limited data, similar to the linguistic exposure of a child, to improve efficiency and accessibility in research.
III. Baby Language Model (BabyLM) Challenge
A. Motivation for BabyLM
1. Diminishing returns and challenges of scaling up models
2. Limited accessibility and high costs of large models
3. Goal: Train smaller models on same linguistic data as a child
B. BabyLM Training Data
1. Developmentally inspired pre-training dataset
2. Less than 100 million words, mostly transcribed speech
3. Mixed-domain to reflect child's language exposure
19:29 🤖 AI agents represent a shift from traditional LLMs, focusing on more dynamic interactions, personalization, and practical applications in various domains.
IV. Agents for Natural Language Processing
A. Thesis: Humans will communicate with AI using natural language
1. Current interaction methods (e.g., websites, UIs) are inefficient
2. AI agents can simplify interactions and act as digital extensions
27:44 🧑💼 Personalization and user-agent alignment are crucial in developing AI agents, ensuring they cater to individual preferences and requirements.
B. Why Build Agents?
1. Chaining, recursion, and multiple calls to LLM are often needed
2. Agents can solve these challenges and act as a computing chip
C. Agent Architecture
1. Key components: memory, tools, planning layer, and interface
2. Example: Browser agent for autonomously booking a flight
D. Levels of Autonomy for Agents
1. Five levels defined, similar to autonomous driving classification
2. Current state of software agents is around Level 2 or 3
3. Transition towards Level 4 and 5 systems expected in the future
31:14 🖥 Computer interactions with AI agents can be achieved through APIs or direct control, each with its advantages and challenges in implementation.
V. Computer Interactions with Agents
A. Two Approaches for Agent-Computer Interaction
1. API-based: Using existing APIs and tools programmatically
a) Advantages: Easy to learn, safe, and controllable
b) Disadvantages: Limited flexibility and potential for errors
2. Direct Interaction: Using keyboard and mouse control
a) Advantages: More performant and flexible
b) Disadvantages: More prone to errors and safety concerns
B. Universal API for Agent Invocation
1. Exploration of a single API to invoke any agent
2. Eliminates the need for individual APIs for each task
VI. Memory and Personalization for Agents
A. Memory for Agents
1. Analogy to disk storage in a computer
2. Challenges: Long-term storage, temporal coherence, and structured data
B. Personalization and User-Agent Alignment
1. Importance of understanding and aligning with user preferences
2. Explicit and implicit learning methods for personalization
3. Challenges: Data collection, feedback mechanisms, and flat application
42:11 🗣 Multi-agent communication and coordination are essential for complex task execution, requiring robust protocols and hierarchical management.
VII. Multi-Agent Systems
A. Advantages of Multi-Agent Systems
1. Parallelization and task specialization
2. Improved efficiency and scalability
B. Challenges in Multi-Agent Communication
1. Communication protocols for effective information exchange
2. Hierarchies and task coordination
3. Robustness against miscommunication and errors
50:22 📈 Future directions in AI agent development include addressing reliability, error correction, security, and user permissions to ensure safe and effective deployment.
VIII. Future Directions and Key Issues
A. Key Issues with Building Autonomous Agents
1. Reliability and error reduction
2. Looping problems and divergence from tasks
3. Testing, benchmarking, and deployment challenges
4. Observability and kill switch mechanisms for safety
B. Computer Abstraction of Agents
1. LM Operating System concept
2. Neural Computer architecture with chat interface, task engine, and routing agent
C. Importance of Error Correction and Security
1. Inherent error correction mechanisms in agent frameworks
2. User permissions and sandboxing for security
3. Safe deployment in risky settings
Last week I started working on agents and this talk came at perfect time. It gave me broader perspective about Agents.
Thank you
Great video! this is what we need to keep moving forward.
Thanks for sharing!😊
Is there any chance that you will share the last lecture of CS25 v3🥺 Very interested in Retrieval Augmentation...
coming soon!
there is also this line of work from my friend 😄
What do you think about the paper, are emergent abilities a mirage?
At 10:47 I am pretty sure there is an error, it should say models of approx. 100M params or more, not 100B
Basically 0 experimental results