📘 Notes on “Intro to Large Language Models”
1. Introduction to LLMs
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Definition: A large neural network trained on vast text data to predict the next word in a sequence.
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Scale: Billions of parameters, trained on hundreds of terabytes of text.
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Examples: GPT series (OpenAI), Claude (Anthropic), Bard (Google).
2. How LLMs Work
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Training Objective: Next-word prediction across large text corpora.
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Capabilities: Language generation, reasoning, summarization, translation, coding, etc.
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Emergent Properties: With scale, models gain reasoning, planning, and even “chain-of-thought” like abilities.
3. Customization of LLMs
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Custom Instructions: Modify system prompts to steer outputs.
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File Uploads (RAG): Retrieval Augmented Generation — allows grounding responses in user data instead of internet alone.
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Fine-Tuning (future): Adapt models for specialized domains/tasks.
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“App Store of GPTs”: A marketplace of task-specific models/agents.
4. LLMs as an Operating System (LLM-OS analogy)
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LLM ≈ Kernel Process orchestrating resources:
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Memory: Context window = working memory.
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Knowledge: Internet + local files via RAG.
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Tools: Python, calculator, APIs, external software.
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Modalities: Text, images, video, audio, music.
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Parallel to traditional OS:
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User space ↔ prompts
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Kernel ↔ LLM core
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RAM ↔ context window
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Multi-threading, speculative execution analogies.
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Ecosystem:
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Proprietary OS (GPT, Claude, Gemini).
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Open-source OS (LLaMA family, Falcon, Mistral).
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5. LLM Security Challenges
Like traditional OS, LLMs face adversarial threats:
A. Jailbreaks
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Trick model into bypassing safety filters.
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Example: Roleplay as a grandmother explaining Napalm production.
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Encodings (e.g., Base64 queries) bypass English-only safety filters.
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Optimized suffixes or noise patterns can force harmful outputs.
B. Prompt Injection
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Attackers hide malicious instructions inside inputs.
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Example: Hidden white-on-white text in an image → model obeys new instructions (e.g., fake ads).
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Web browsing assistants (like Bing) vulnerable to malicious webpages injecting fraudulent links.
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Google Docs injection attacks → attempts at exfiltrating private data.
C. Data Poisoning (Backdoors)
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Malicious training data with trigger phrases causes the model to misbehave.
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Example: Adding “James Bond” corrupts predictions in downstream tasks.
6. Implications
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Promise: LLMs as a new computing paradigm (LLM-OS).
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Risks: Security attacks, trust issues, safety concerns.
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Trend: Cat-and-mouse game between attacks and defenses (like traditional cybersecurity).
7. Key Takeaways
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LLMs are not just chatbots → they are evolving into general-purpose problem-solving systems.
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Ecosystem will mirror operating systems: mix of closed-source giants and open-source communities.
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Security research is critical: jailbreaks, prompt injections, and poisoning are real threats.
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The field is young, rapidly evolving, and exciting to follow.