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introtoLLMconcepts-limits.md(Datei erstellt)
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1 | + | # A Short Personal Reference Guide to LLM Concepts & Limitations | |
2 | + | ||
3 | + | ## Core Concepts | |
4 | + | ||
5 | + | ### Foundation | |
6 | + | - **Large Language Models**: Neural networks trained on vast text data to predict next tokens | |
7 | + | - **Architecture**: Primarily transformer-based with attention mechanisms | |
8 | + | - **Parameters**: Model size/capacity (billions to trillions) | |
9 | + | - **Training**: Self-supervised learning on text corpora | |
10 | + | - **Inference**: Generation of text responses to prompts | |
11 | + | ||
12 | + | ### Key Capabilities | |
13 | + | - Text comprehension and generation | |
14 | + | - Pattern recognition across diverse domains | |
15 | + | - In-context learning from examples | |
16 | + | - Reasoning through chain-of-thought processes | |
17 | + | - Knowledge retrieval from training data | |
18 | + | ||
19 | + | ### Interaction Methods | |
20 | + | - **Prompting**: Crafting effective instructions | |
21 | + | - **RAG**: Retrieval-Augmented Generation for factual grounding | |
22 | + | - **Fine-tuning**: Adapting models to specific tasks/domains | |
23 | + | - **Function calling**: Enabling tool use and API integration | |
24 | + | ||
25 | + | ## Critical Limitations | |
26 | + | ||
27 | + | ### Knowledge Constraints | |
28 | + | - **Knowledge cutoff**: Limited to training data timeframe | |
29 | + | - **Hallucinations**: Confidently generating false information | |
30 | + | - **Shallow expertise**: Broad but often superficial domain knowledge | |
31 | + | ||
32 | + | ### Reasoning Gaps | |
33 | + | - **Mathematical reasoning**: Struggles with complex calculations | |
34 | + | - **Spatial reasoning**: Limited understanding of physical space/geometry | |
35 | + | - **Causal reasoning**: Correlation vs. causation confusion | |
36 | + | - **Logical consistency**: Contradictions across longer contexts | |
37 | + | ||
38 | + | ### Context Handling | |
39 | + | - **Context window**: Limited text history (tokens) maintained | |
40 | + | - **Memory**: No persistent memory between sessions | |
41 | + | - **World model**: Incomplete understanding of physical reality | |
42 | + | ||
43 | + | ### Social Limitations | |
44 | + | - **Theory of mind**: Limited understanding of human intentions/beliefs | |
45 | + | - **Cultural nuance**: Struggles with complex cultural contexts | |
46 | + | - **Ethical reasoning**: Simplified moral frameworks | |
47 | + | ||
48 | + | ### Technical Constraints | |
49 | + | - **Latency**: Generation speed limitations | |
50 | + | - **Computing costs**: Resource intensity of large models | |
51 | + | - **Error recovery**: Difficulty recognizing and correcting mistakes | |
52 | + | - **Multimodal integration**: Limited understanding across modalities | |
53 | + | ||
54 | + | ## Mitigation Strategies | |
55 | + | ||
56 | + | ### Enhancing Reliability | |
57 | + | - Tool integration for verified calculations/lookups | |
58 | + | - Chain-of-thought prompting for complex reasoning | |
59 | + | - Knowledge retrieval from external databases | |
60 | + | - Explicit verification steps for factual claims | |
61 | + | ||
62 | + | ### Improving Output | |
63 | + | - Clear instructions with specific format expectations | |
64 | + | - Example-based prompting for consistency | |
65 | + | - Breaking complex tasks into simpler components | |
66 | + | - Focused domain-specific prompting | |
67 | + | ||
68 | + | ### Safety Practices | |
69 | + | - Avoid overreliance on LLM outputs for critical decisions | |
70 | + | - Verify factual claims through independent sources | |
71 | + | - Maintain human oversight for sensitive applications | |
72 | + | - Recognize appropriate use cases and limitations |
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