IVQA 651-700
651. Tradeoffs of embedded vs. cloud LLMs:
Embedded: Lower latency, greater privacy, offline availability, but limited by compute, memory, and power.
Cloud: Access to larger models, better scalability, but dependent on internet, higher latency, and potential privacy concerns.
652. Deploying quantized LLM on mobile:
Use quantized formats (e.g., 4-bit GGUF), convert model using tools like
transformers,ggml, oronnxruntime, and run via mobile-optimized inference engines (e.g., MLC, llama.cpp, or Core ML).
653. Role of 4-bit/8-bit quantization:
Reduces model size and memory bandwidth needs. Allows edge inference on CPUs/NPUs but may slightly reduce precision. Crucial for running LLMs in RAM-constrained devices.
654. Minimizing memory footprint:
Apply quantization, use smaller architectures (e.g., Phi-2, TinyLLaMA), load model in chunks (streaming), and minimize context length.
655. ONNX vs. GGUF:
ONNX: General-purpose, supports many hardware backends, standard across ML ecosystem.
GGUF: Optimized for LLMs, supports quantized formats and fast inference with llama.cpp or MLC.
656. Offline caching:
Use local storage (SQLite, file cache) with hashed keys (prompt+params). Cache embeddings or full outputs. Use TTL (time-to-live) and LRU (least-recently-used) eviction.
657. Phi-2/TinyLLaMA vs GPT-3.5:
Smaller, more efficient, ideal for constrained environments. Tradeoff in reasoning depth and fluency but useful for simple tasks, classification, or embedded apps.
658. On-device RAG:
Use lightweight vector DBs (e.g., Chroma, Qdrant-lite). Store embeddings locally, use cosine similarity, and fetch context from device storage for grounding.
659. Edge-cloud sync architecture:
Use federated learning or periodic cloud sync. Maintain local inference and updates, then sync user data/feedback to central server.
660. Fallback for low-power clients:
Use prompt simplification, compression, or switch to rule-based systems. Optionally offload to cloud with reconnect/retry logic.
661. Meta-prompting:
Prompting the model to reason about its own output or goals (e.g., "Before answering, plan the steps"). Helps improve coherence and performance.
662. LLM quality assessment:
Self-reflection, likelihood scoring, comparison against known good outputs, or user feedback analysis.
663.Self-refinement:
Model re-evaluates and rewrites its own output iteratively to improve quality, often using a feedback loop or critique-prompt.
664. Evaluating critique loops:
Measure improvements in BLEU, ROUGE, accuracy, or human evaluation between initial and refined responses.
665. Prompt rewriting by model:
Use examples, metadata, or chain-of-thought to let the model modify the prompt for clarity, domain focus, or user intent alignment.
666. Learning without labeled data:
Use user interaction signals (clicks, edits, time spent), weak supervision, or reward models for preference alignment.
667. Prompt evolution:
Maintain a prompt history with feedback. Fine-tune prompt templates over time or auto-generate variants with higher success scores.
668. Policy-gradient updates:
Reinforcement learning approach where the model updates policy (output behavior) based on reward feedback rather than ground truth.
669. Identifying need for help:
Detect ambiguity, uncertainty (low confidence), repeated failures, or user dissatisfaction and escalate to human or higher-capacity model.
670. Safety concerns:
Risks include reward hacking, model drift, harmful output reinforcement, lack of oversight, and unintended behavior from unsupervised learning.
671. Financial advice tuning:
Use supervised fine-tuning on compliant data, reinforce conservative outputs, and enforce disclaimers or approval checks.
672. Legal drafting guardrails:
Insert citation demands, enforce source grounding, limit hallucination via retrieval, and add legal term constraints.
673. Medical LLM validation:
Clinical review, benchmark against known diagnosis sets, use specialist human-in-the-loop, and evaluate using precision/recall.
674. Prompting for code:
Embedded: Constrained APIs, memory limits, safety-focused. Full-stack: Larger context, architectural reasoning, multi-language support.
675. Legal case summarization:
Evaluate with legal experts, cross-check citations, and use summarization quality metrics like ROUGE and factual consistency.
676. Pharma research support:
Extract structured entities, auto-generate trial summaries, validate with SME feedback, integrate with document repositories.
677. Academic LLM evaluation:
Use rubric-based human review, citation accuracy, novelty, coherence, and clarity as metrics.
678. Taxonomy incorporation:
Use structured prompts, knowledge graphs, or embedding-based reranking with domain taxonomies.
679.Scientific writing risks:
Hallucination, outdated references, false precision, and over-simplification. Needs fact-checking and SME review.
680. Patent/IP support:
Use structured prompts, keyword guidance, include claims/examples, and validate via overlap with prior art.
681.Whisper vs. traditional STT:
Whisper vs. traditional STT:
Whisper uses transformers with multilingual, zero-shot capability; better accuracy across accents/languages but heavier.
Transcription + summarization:
Pipeline: Audio → STT (e.g., Whisper) → Chunking → LLM summarization (e.g., GPT). Enables podcast notes or meeting digests.
TTS integration:
Consider latency, tone consistency, speaker variation, and model size. Use Edge TTS or Coqui.
Aligning visuals with scripts:
Use scene segmentation, object recognition, and LLM-generated scene descriptions to guide visuals.
Real-time captioning:
Combine STT (Whisper or DeepSpeech) with lightweight summarizers or text formatters. Add latency buffers.
Multimodal input workflows:
Use gesture/image classifiers, ASR, and natural language inputs as multi-source events. Requires unified model or orchestration.
Role of VLMs:
Vision-Language Models like GPT-4V can jointly reason over images and text, enabling OCR, image Q&A, diagram understanding.
Prompting image generators:
Use structured prompt templates, style references, and consistent descriptors. Validate with test outputs.
Video editing use cases:
Auto-captioning, clip trimming, scene reordering, style transformation, background substitution, and highlight reels.
Narrated video from documents:
Chunk doc → summarize → script generation → TTS → visuals → video stitching (using tools like Runway, Descript).
Section 70: Future Architectures & Emerging Ideas
Mixture of experts (MoE):
Activates only relevant subnetworks during inference, allowing scalability without full compute cost. Used in models like GShard and Switch Transformer.
Recurrent Memory Transformer:
Adds memory units to standard Transformers for longer context and state retention. Improves long-sequence reasoning.
State-space models (SSMs):
Process sequences with linear recurrence and memory, enabling efficient long-context handling (e.g., Mamba, S4).
Mamba, RWKV vs Transformers:
Mamba and RWKV use state-space or RNN-like mechanisms for better scalability and inference efficiency on long texts, outperforming Transformers in memory use.
FlashAttention:
A fast, memory-efficient attention mechanism that improves training/inference speed by optimizing memory access patterns.
Sparsity techniques:
Skip irrelevant computations via sparse activations or weights. Maintains performance while reducing cost (used in MoE, pruning).
Retrieval as first-class citizen:
Emphasizes hybrid systems (RAG, Toolformer) where retrieval augments model context. Improves grounding and performance.
Modularity:
Decouples components (retrieval, generation, memory, tools). Enables faster iteration, debugging, and reuse.
Agent-based modeling:
Uses LLMs as reasoning agents with autonomy, memory, and tools. Advances autonomy via looped planning-execution-reflection.
Convergence with cognitive architectures:
Trends toward combining symbolic reasoning, memory modules, learning loops, and agent-like behavior for human-like AI systems.
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