Table of Contents
🔹 Overview
🔹 Lesson Start
🔹 Practice
🔹 Comprehension Quiz
🔹 Summary
🔹 Quiz Answers
🔍 Overview In this lesson, we explore how large language models (LLMs) struggle with multi-turn conversations. We’ll learn why LLMs often go off-track when instructions are added step-by-step, and how to overcome this limitation with effective strategies.
📖 Lesson Start
🗣 (S) Hmm… I’m having a bit of trouble.
🎓 (T) What’s going on? Is your AI tool not working as expected?
🗣 (S) Yeah… Sometimes, when I give it additional instructions across several turns, the answers get weird or confusing. Even though I try to explain things step by step, the results just don’t improve.
🎓 (T) Ah, you’ve hit one of the common problems with LLMs. This happens to a lot of people. Let me explain using a recent research paper titled “LLMs Get Lost in Multi-Turn Conversation”.
🗣 (S) Sounds serious! So, what’s going wrong exactly?
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