MLG 035 Large Language Models 2

Learn Faster with a Walking DeskWalk While You Learn

Sitting for hours drains energy and focus. A walking desk boosts alertness, helping you retain complex ML topics more effectively.Boost focus and energy to learn faster and retain more.Discover the benefitsDiscover the benefits

In-Context Learning (ICL)

• Definition: LLMs can perform tasks by learning from examples provided directly in the prompt without updating their parameters.
  • Types:
    • Zero-shot: Direct query, no examples provided.
    • One-shot: Single example provided.
    • Few-shot: Multiple examples, balancing quantity with context window limitations.
  • Mechanism: ICL works through analogy and Bayesian inference, using examples as semantic priors to activate relevant internal representations.
  • Emergent Properties: ICL is an "inference-time training" approach, leveraging the model’s pre-trained knowledge without gradient updates; its effectiveness can be enhanced with diverse, non-redundant examples.

Retrieval Augmented Generation (RAG) and Grounding

• Grounding: Connecting LLMs with external knowledge bases to supplement or update static training data.
  • Motivation: LLMs’ training data becomes outdated or lacks proprietary/specialized knowledge.
  • Benefit: Reduces hallucinations and improves factual accuracy by incorporating current or domain-specific information.
• RAG Workflow:
  1. Embedding: Documents are converted into vector embeddings (using sentence transformers or representation models).
  2. Storage: Vectors are stored in a vector database (e.g., FAISS, ChromaDB, Qdrant).
  3. Retrieval: When a query is made, relevant chunks are extracted based on similarity, possibly with re-ranking or additional query processing.
  4. Augmentation: Retrieved chunks are added to the prompt to provide up-to-date context for generation.
  5. Generation: The LLM generates responses informed by the augmented context.
  • Advanced RAG: Includes agentic approaches - self-correction, aggregation, or multi-agent contribution to source ingestion, and can integrate external document sources (e.g., web search for real-time info, or custom datasets for private knowledge).

LLM Agents

• Overview: Agents extend LLMs by providing goal-oriented, iterative problem-solving through interaction, memory, planning, and tool usage.
• Key Components:
  • Reasoning Engine (LLM Core): Interprets goals, states, and makes decisions.
  • Planning Module: Breaks down complex tasks using strategies such as Chain of Thought or ReAct; can incorporate reflection and adjustment.
  • Memory: Short-term via context window; long-term via persistent storage like RAG-integrated databases or special memory systems.
  • Tools and APIs: Agents select and use external functions - file manipulation, browser control, code execution, database queries, or invoking smaller/fine-tuned models.
• Capabilities: Support self-evaluation, correction, and multi-step planning; allow integration with other agents (multi-agent systems); face limitations in memory continuity, adaptivity, and controllability.
• Current Trends: Research and development are shifting toward these agentic paradigms as LLM core scaling saturates.

Multimodal Large Language Models (MLLMs)

• Definition: Models capable of ingesting and generating across different modalities (text, image, audio, video).
• Architecture:
  • Modality-Specific Encoders: Convert raw modalities (text, image, audio) into numeric embeddings (e.g., vision transformers for images).
  • Fusion/Alignment Layer: Embeddings from different modalities are projected into a shared space, often via cross-attention or concatenation, allowing the model to jointly reason about their content.
  • Unified Transformer Backbone: Processes fused embeddings to allow cross-modal reasoning and generates outputs in the required format.
• Recent Advances: Unified architectures (e.g., GPT-4o) use a single model for all modalities rather than switching between separate sub-models.
• Functionality: Enables actions such as image analysis via text prompts, visual Q&A, and integrated speech recognition/generation.

Advanced LLM Architectures and Training Directions

• Predictive Abstract Representation: Incorporating latent concept prediction alongside token prediction (e.g., via autoencoders).
• Patch-Level Training: Predicting larger “patches” of tokens to reduce sequence lengths and computation.
• Concept-Centric Modeling: Moving from next-token prediction to predicting sequences of semantic concepts (e.g., Meta’s Large Concept Model).
• Multi-Token Prediction: Training models to predict multiple future tokens for broader context capture.

Evaluation Benchmarks (as of 2025)

• Key Benchmarks Used for LLM Evaluation:
  • GPQA (Diamond): Graduate-level STEM reasoning.
  • SWE Bench Verified: Real-world software engineering, verifying agentic code abilities.
  • MMMU: Multimodal, college-level cross-disciplinary reasoning.
  • HumanEval: Python coding correctness.
  • HLE (Human’s Last Exam): Extremely challenging, multimodal knowledge assessment.
  • LiveCodeBench: Coding with contamination-free, up-to-date problems.
  • MLPerf Inference v5.0 Long Context: Throughput/latency for processing long contexts.
  • MultiChallenge Conversational AI: Multiturn dialogue, in-context reasoning.
  • TAUBench/PFCL: Tool utilization in agentic tasks.
  • TruthfulnessQA: Measures tendency toward factual accuracy/robustness against misinformation.

Prompt Engineering: High-Impact Techniques

• Foundational Approaches:
  • Few-Shot Prompting: Provide pairs of inputs and desired outputs to steer the LLM.
  • Chain of Thought: Instructing the LLM to think step-by-step, either explicitly or through internal self-reprompting, enhances reasoning and output quality.
  • Clarity and Structure: Use clear, detailed, and structured instructions - task definition, context, constraints, output format, use of delimiters or markdown structuring.
  • Affirmative Directives: Phrase instructions positively (“write a concise summary” instead of “don’t write a long summary”).
  • Iterative Self-Refinement: Prompt the LLM to review and improve its prior response for better completeness, clarity, and factuality.
  • System Prompt/Role Assignment: Assign a persona or role to the LLM for tailored behavior (e.g., “You are an expert Python programmer”).
• Guideline: Regularly consult official prompting guides from model developers as model capabilities evolve.

Trends and Research Outlook

• Inference-time compute is increasingly important for pushing the boundaries of LLM task performance.
• Agentic LLMs and multimodal reasoning represent the primary frontiers for innovation.
• Prompt engineering and benchmarking remain essential for extracting optimal performance and assessing progress.
• Models are expected to continue evolving with research into new architectures, memory systems, and integration techniques.