tutorials:ai_llm_tools
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| tutorials:ai_llm_tools [2025/06/10 23:21] – [Additional Points] chkuo | tutorials:ai_llm_tools [2026/05/08 23:14] (current) – [Preface] chkuo | ||
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| ===== About ===== | ===== About ===== | ||
| - | * A brief guide to using large language models (LLMs) often referred to as artificial intelligence (AI) tools, such as ChatGPT | + | * A brief guide to using large language models (LLMs) often referred to as artificial intelligence (AI) tools, such as ChatGPT/Claude/Gemini, in the context of academic research and writing. By Chih-Horng Kuo (chk@gate.sinica.edu.tw). Suggestions are welcome. |
| * Target audience: undergraduate students, graduate students, and postdocs (in biology) | * Target audience: undergraduate students, graduate students, and postdocs (in biology) | ||
| + | |||
| ===== Preface ===== | ===== Preface ===== | ||
| + | * Privacy, both regarding institutional rules and personal reasons, is important | ||
| * For work: Ethics first! | * For work: Ethics first! | ||
| * Respect the general rules of academic integrity | * Respect the general rules of academic integrity | ||
| * As the user, you are responsible for the final output of your work | * As the user, you are responsible for the final output of your work | ||
| * Include proper usage statements | * Include proper usage statements | ||
| - | * For personal use: It is possible | + | * For personal use: |
| + | * Emotional support can be valuable | ||
| + | * Should | ||
| + | * Be careful of potential misuse and harm | ||
| ===== LLMs Explained ===== | ===== LLMs Explained ===== | ||
| ==== 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 |
| + | | ||
| + | * 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, | ||
| ==== Value ==== | ==== Value ==== | ||
| - | * Always-available conversation | + | * Always-available conversation |
| + | * Often, just having a conversation helps you think much more clearly | ||
| + | * Particularly helpful in the context of emotional support | ||
| * In some ways, LLMs are like crystals of human knowledge | * In some ways, LLMs are like crystals of human knowledge | ||
| - | | + | |
| - | * Perform especially well on topics with abundant, high-quality training data | + | * Perform especially well on topics with abundant, high-quality training data |
| * Highly proficient in language, particularly useful for clear and precise expression of ideas | * Highly proficient in language, particularly useful for clear and precise expression of ideas | ||
| + | * Great tools for self-improvement | ||
| + | * How much can LLMs be trusted? I don't know | ||
| + | * Have I successfully used LLMs to achieve deeper thinking and clearer writing? YES! | ||
| + | * What's the catch? Time and effort | ||
| Line 48: | Line 62: | ||
| * LLMs do not know what they do not know; may make up false info (AI hallucination) | * LLMs do not know what they do not know; may make up false info (AI hallucination) | ||
| * For scientific writing | * For scientific writing | ||
| - | * May not be suitable | + | |
| + | | ||
| ==== Common Failure Modes ==== | ==== Common Failure Modes ==== | ||
| - | * Non-specific terminology | ||
| * Superficial content | * Superficial content | ||
| - | * Over simplification | + | * Oversimplification |
| * Overstatement | * Overstatement | ||
| * False coherence | * False coherence | ||
| * Hallucination; | * Hallucination; | ||
| + | * Non-specific terminology for highly specific topics | ||
| Line 62: | Line 78: | ||
| * Use LLMs as tools to sharpen your thinking, not black boxes for quick answers | * Use LLMs as tools to sharpen your thinking, not black boxes for quick answers | ||
| * Do not outsource your thinking process! | * Do not outsource your thinking process! | ||
| + | * Use LLMs in your thinking and learning: you become better | ||
| + | * Use LLMs to think for you: you skip the practice, bad for learning | ||
| * LLMs may not save time | * LLMs may not save time | ||
| * In fact, LLMs are most valuable when used to deepen the thinking process | * In fact, LLMs are most valuable when used to deepen the thinking process | ||
| * Explore possibilities, | * Explore possibilities, | ||
| - | * For example: given the abstract, (1) suggest a title, or (2) suggest three titles, compare the emphasis and tone, refine to express accurately, adjust based on the target audience | + | * Example: You are deciding the title of your manuscript. You provided |
| + | * (1) suggest a title, or | ||
| + | * (2) suggest three titles, compare the emphasis and tone, refine to express accurately, adjust based on the target audience | ||
| * LLMs act like mirrors; their responses reflect your input and framing | * LLMs act like mirrors; their responses reflect your input and framing | ||
| * Trained to be agreeable, they often conform to both your explicit instructions and implicit tone | * Trained to be agreeable, they often conform to both your explicit instructions and implicit tone | ||
| - | | + | |
| - | * Start with a solid frame of reference | + | |
| - | * Provide clear context for the task, including your goals and expectations | + | |
| - | * What exactly do you want to obtain? | + | |
| - | * How do you want the LLM to act? | + | |
| - | * Are your expectations realistic? | + | |
| - | * Examples of possible uses (varying in feasibility and reliability): | + | |
| - | * Polish arguments | + | |
| - | * Organize | + | |
| - | * Brainstorm new ideas | + | |
| - | * Identify major gaps in reasoning | + | |
| - | * Provide critical review | + | |
| - | * Assess the output, make decisions, | + | |
| - | * Use an iterative process; may involve gradual refinement or drastic changes | + | |
| - | * Fact check, fact check, and fact check | + | |
| - | * Know when to stop; what did you get in the last iteration? | + | |
| + | ===== Suggested practice ===== | ||
| + | * Start with a solid frame of reference | ||
| + | * Provide clear context for the task, including your goals and expectations | ||
| + | * Learn the basic of markdown can be very helpful ("help me to learn the very basic of markdown language in 5 minutes, explain to me how can I use this in my prompt to improve the clarity of my instruction to you") | ||
| + | * What exactly do you want to obtain? | ||
| + | * How do you want the LLM to act? | ||
| + | * Are your expectations realistic? | ||
| + | * Examples of possible uses (varying in feasibility and reliability): | ||
| + | * Polish arguments | ||
| + | * Organize thoughts | ||
| + | * Brainstorm new ideas | ||
| + | * Identify major gaps in reasoning | ||
| + | * Provide critical review | ||
| + | * Assess the output, make decisions, and provide feedback | ||
| + | * Use an iterative process; may involve gradual refinement or drastic changes | ||
| + | * Fact check, fact check, and fact check!!! | ||
| + | * 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 ===== | ||
| Line 99: | Line 124: | ||
| ===== 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) | ||
| - | * Different tools for different uses | + | * Different tools for different uses; 2026 impressions: |
| - | * ChatGPT is chatty; highly conversational, | + | * ChatGPT is chatty; highly conversational, |
| - | * Gemini integrates | + | * Claude is often more "get to the point" compares to ChatGPT |
| + | * Gemini integrates | ||
| * Learn to judge output, know when to reject the suggestions | * Learn to judge output, know when to reject the suggestions | ||
| * Over time, thoughtful use of LLMs can improve your writing, reflection, and decision-making skills | * Over time, thoughtful use of LLMs can improve your writing, reflection, and decision-making skills | ||
| * AI/LLM tools are under intensive development, | * AI/LLM tools are under intensive development, | ||
| + | * More advanced tools, such as Claude Cowork/ | ||
tutorials/ai_llm_tools.1749568877.txt.gz · Last modified: by chkuo