tutorials:ai_llm_tools
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| tutorials:ai_llm_tools [2025/06/12 17:40] – [Value] chkuo | tutorials:ai_llm_tools [2025/09/15 18:00] (current) – [Additional Points] chkuo | ||
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| ==== What are LLMs? ==== | ==== What are LLMs? ==== | ||
| - | * Artificial intelligence systems trained on large collections of text to generate human-like responses | ||
| - | * Predict the next word (token) based on statistical patterns | ||
| - | * Do not “think” or “understand” like humans | ||
| - | * Mimic intelligence, | ||
| * Key concept | * Key concept | ||
| * LLMs work by estimating probability relationships among tokens, not by indexing content like a database such as Wikipedia | * LLMs work by estimating probability relationships among tokens, not by indexing content like a database such as Wikipedia | ||
| * Key terms | * Key terms | ||
| - | * Token: basic unit for LLMs to process | + | * Token: |
| * Prompt: user input | * Prompt: user input | ||
| * Session: a series of user input and LLM output | * Session: a series of user input and LLM output | ||
| * Good practice: limit the scope and length of individual sessions | * Good practice: limit the scope and length of individual sessions | ||
| * Memory: information kept from previous interactions; | * Memory: information kept from previous interactions; | ||
| + | * Artificial intelligence systems trained on large collections of text to generate human-like responses | ||
| + | * Human: form thoughts first, then use language to communicate those thoughts | ||
| + | * LLM: generate language by predict the next word (token) based on statistical patterns | ||
| + | * LLMs do not “think” or “understand” like humans do; the appearance of “thought” is a byproduct of those patterns | ||
| + | * LLMs mimic intelligence, | ||
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| * Fact check, fact check, and fact check!!! | * Fact check, fact check, and fact check!!! | ||
| * Know when to stop; did the last iteration provide meaningful improvement? | * Know when to stop; did the last iteration provide meaningful improvement? | ||
| + | * Exercises | ||
| + | * Use figure legends as input, ask for a manuscript outline as output; explore different ways of ordering the figures | ||
| + | * Use a poster abstract as input, ask for possible titles as output; compare how each emphasizes different aspects of the work or addresses different audiences | ||
| ===== Human–AI Dynamics ===== | ===== Human–AI Dynamics ===== | ||
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| ===== Use Cases ===== | ===== Use Cases ===== | ||
| - | * Writing and obtaining feedback as a way of thinking; force LLMs to criticize and challenge your thoughts | + | * Force LLMs to criticize and challenge your thoughts |
| * Use the "Deep Research" | * Use the "Deep Research" | ||
| - | * Reading | + | * Help to read dense papers |
| * Build custom databases of selected references using NotebookLM by Google | * Build custom databases of selected references using NotebookLM by Google | ||
| - | * Grammar | + | * Perform grammar |
| * Prepare for discussion with seminar speakers; info about you and the speaker as the input, suggestions of questions to ask as the output | * Prepare for discussion with seminar speakers; info about you and the speaker as the input, suggestions of questions to ask as the output | ||
| - | * Drafting | + | * Draft or refine |
| - | * Technology | + | * Obtain technology-related |
| - | ===== Additional Points | + | ===== Practical Advice & Next Steps ===== |
| * Test the tool with topics that you know well first for evaluation | * Test the tool with topics that you know well first for evaluation | ||
| * Push boundaries, learn the situations that LLMs are useful (or not) | * Push boundaries, learn the situations that LLMs are useful (or not) | ||
tutorials/ai_llm_tools.1749721253.txt.gz · Last modified: by chkuo